Datasets:

davanstrien

/

model_cards_with_readmes

like 1

facebook/wmt21-dense-24-wide-en-x

facebook

m2m_100

transformers

translation

mit

['multilingual', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de', 'en']

['translation', 'wmt21']

# WMT 21 En-X WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2108.03265) and first released in [this](https://github.com/pytorch/fairseq/tree/main/examples/wmt21) repository. The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de). To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. *Note: `M2M100Tokenizer` depends on `sentencepiece`, so make sure to install it before running the example.* To install `sentencepiece` run `pip install sentencepiece` Since the model was trained with domain tags, you should prepend them to the input as well. * "wmtdata newsdomain": Use for sentences in the news domain * "wmtdata otherdomain": Use for sentences in all other domain ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("facebook/wmt21-dense-24-wide-en-x") tokenizer = AutoTokenizer.from_pretrained("facebook/wmt21-dense-24-wide-en-x") inputs = tokenizer("wmtdata newsdomain One model for many languages.", return_tensors="pt") # translate English to German generated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.get_lang_id("de")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "Ein Modell für viele Sprachen." # translate English to Icelandic generated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.get_lang_id("is")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "Ein fyrirmynd fyrir mörg tungumál." ``` See the [model hub](https://huggingface.co/models?filter=wmt21) to look for more fine-tuned versions. ## Languages covered English (en), Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de) ## BibTeX entry and citation info ``` @inproceedings{tran2021facebook title={Facebook AI’s WMT21 News Translation Task Submission}, author={Chau Tran and Shruti Bhosale and James Cross and Philipp Koehn and Sergey Edunov and Angela Fan}, booktitle={Proc. of WMT}, year={2021}, } ```

e6801e52b08ba47b4a091ac3bd5b73ed

IMSyPP/hate_speech_it

IMSyPP

bert

transformers

text-classification

mit

['it']

[]

# Hate Speech Classifier for Social Media Content in Italian Language A monolingual model for hate speech classification of social media content in Italian language. The model was trained on 119,670 YouTube comments and tested on an independent test set of 21,072 YouTube comments. It is based on Italian ALBERTO pre-trained language model. ## Tokenizer During training the text was preprocessed using the original Italian ALBERTO tokenizer. We suggest the same tokenizer is used for inference. ## Model output The model classifies each input into one of four distinct classes: * 0 - acceptable * 1 - inappropriate * 2 - offensive * 3 - violent

2d246f7625b8a4e4c498e31cb2d0e93c

kadirnar/AnimeSR_Paper_Model

kadirnar

object-detection

apache-2.0

['object-detection', 'computer-vision', 'gan', 'animegan']

### Model Description [AnimeSR](https://arxiv.org/abs/2206.07038): Learning Real-World Super-Resolution Models for Animation Videos ### Installation ``` pip install animesr ``` ### Anime GAN ```python from animesr.inference_animesr_video import main main(source='test.mp4', 'kadirnar/AnimeSR_Paper_Model') ``` ### BibTeX Entry and Citation Info ``` @InProceedings{wu2022animesr, author={Wu, Yanze and Wang, Xintao and Li, Gen and Shan, Ying}, title={AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos}, booktitle={Advances in Neural Information Processing Systems}, year={2022} } ```

95d5089787beeaa0cd245e516b052863

chuchun9/distilbert-base-uncased-finetuned-squad

chuchun9

distilbert

transformers

question-answering

apache-2.0

['squad']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.6727 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5227 | 1.0 | 1107 | 2.0485 | | 1.7555 | 2.0 | 2214 | 1.7443 | | 1.4567 | 3.0 | 3321 | 1.6511 | | 1.2107 | 4.0 | 4428 | 1.6496 | | 1.083 | 5.0 | 5535 | 1.6727 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

959278c3725aed02c22b53080c87593a

shivi/sd-album-covers

shivi

diffusers

text-to-image

creativeml-openrail-m

['text-to-image']

### sd-album-covers Dreambooth model trained by shivi with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: Taylor Swift Red Album Cover (use that on your prompt) Santana Africa Speaks Album Cover (use that on your prompt) Beatles Abbey Road Album Cover (use that on your prompt) Led Zepellin Celebration Day album cover (use that on your prompt) Maroon5 band Overexposed music album cover (use that on your prompt) Metallica Harvester of Sorrow music album cover (use that on your prompt) Linkin Park band logo (use that on your prompt) 

0518e74ccd4f7fedf2899045b2a4c9f0

espnet/wanchichen_fleurs_asr_conformer_scctc

espnet

espnet

cc-by-4.0

['en']

['google/fleurs']

['espnet', 'audio', 'speech-recognition']

## ESPnet2 ASR model ### `espnet/wanchichen_fleurs_asr_conformer_sctctc` This model was trained by William Chen using the fleurs recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet pip install -e . cd egs2/fleurs/asr1 ./run.sh ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Sat Oct 22 14:55:21 EDT 2022` - python version: `3.8.6 (default, Dec 17 2020, 16:57:01) [GCC 10.2.0]` - espnet version: `espnet 202207` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `e534106b837ff6cdd29977a52983c022ff1afb0f` - Commit date: `Sun Sep 11 22:31:23 2022 -0400` ## asr_train_asr_xlsr_conformer_scctc_raw_all_bpe6500_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave_3best/test_all|77809|1592160|70.5|26.1|3.4|3.4|32.9|97.0| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave_3best/test_all|77809|10235271|92.2|4.7|3.1|2.6|10.4|97.0| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave_3best/test_all|77809|9622352|91.3|5.6|3.1|2.7|11.4|97.0|

20def58ff860df51c9ffa070273edc4f

JorisCos/DCCRNet_Libri1Mix_enhsingle_16k

JorisCos

asteroid

audio-to-audio

cc-by-sa-4.0

['Libri1Mix', 'enh_single']

['asteroid', 'audio', 'DCCRNet', 'audio-to-audio', 'speech-enhancement']

## Asteroid model `JorisCos/DCCRNet_Libri1Mix_enhsignle_16k` Description: This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the `enh_single` task of the Libri1Mix dataset. Training config: ```yml data: n_src: 1 sample_rate: 16000 segment: 3 task: enh_single train_dir: data/wav16k/min/train-360 valid_dir: data/wav16k/min/dev filterbank: stft_kernel_size: 400 stft_n_filters: 512 stft_stride: 100 masknet: architecture: DCCRN-CL n_src: 1 optim: lr: 0.001 optimizer: adam weight_decay: 1.0e-05 training: batch_size: 12 early_stop: true epochs: 200 gradient_clipping: 5 half_lr: true num_workers: 4 ``` Results: On Libri1Mix min test set : ```yml si_sdr: 13.329767398333798 si_sdr_imp: 9.879986092474098 sdr: 13.87279932997016 sdr_imp: 10.370136530757103 sir: Infinity sir_imp: NaN sar: 13.87279932997016 sar_imp: 10.370136530757103 stoi: 0.9140907015623948 stoi_imp: 0.11817087802185405 ``` License notice: This work "DCCRNet_Libri1Mix_enhsignle_16k" is a derivative of [LibriSpeech ASR corpus](http://www.openslr.org/12) by Vassil Panayotov, used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/); of The WSJ0 Hipster Ambient Mixtures dataset by [Whisper.ai](http://wham.whisper.ai/), used under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (Research only). "DCCRNet_Libri1Mix_enhsignle_16k" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/) by Joris Cosentino

fa8a452225fe528568f044161cc0c6ab

flax-community/gpt-neo-1.3B-apps

flax-community

gpt_neo

transformers

text-generation

mit

['en', 'python']

['apps']

['gpt_neo', 'code_synthesis']

# GPT-Neo-1.3B-APPS > **Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot** ## Model Description GPT-Neo-1.3B-APPS is a GPT-Neo-125M finetuned on APPS dataset. This model is specialized to solve programming tasks. ## Training data The model is trained on the [Automated Programming Progress Standard (APPS) dataset](https://github.com/hendrycks/apps). The dataset consists of 10,000 coding problems in total, with 131,836 test cases for checking solutions and 232,444 ground-truth solutions written by humans. Problems can be complicated, as the average length of a problem is 293.2 words. The data are split evenly into training and test sets, with 5,000 problems each. This model is fine-tuned using most of the APPS dataset including both train and test split to explore the impact of this training task on model performance on other code synthesis evaluation metrics. A model fine-tuned on train set only can be found [here](https://huggingface.co/flax-community/gpt-neo-125M-apps). ## Training procedure The training script used to train this model can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/run_clm_apps.py). Training is done for 5 epochs using AdamW optimizer and leaner decay learning rate schedule with 800 warmup steps. To reproduce the training one can use this command with the above script: ```bash python run_clm_apps.py \ --output_dir $HOME/gpt-neo-1.3B-apps \ --model_name_or_path EleutherAI/gpt-neo-1.3B \ --dataset_name $HOME/gpt-code-clippy/data_processing/apps.py \ --dataset_config_name formatted \ --do_train --do_eval \ --block_size="1024" \ --per_device_train_batch_size="3" \ --per_device_eval_batch_size="3" \ --preprocessing_num_workers="16" \ --learning_rate="8e-5" \ --warmup_steps="800" \ --adam_beta1="0.9" \ --adam_beta2="0.98" \ --weight_decay="0.1" \ --overwrite_output_dir \ --num_train_epochs="5" \ --logging_steps="50" \ --eval_steps="2000" \ --report_to="wandb" \ --dtype="bfloat16" \ --save_strategy epoch \ --gradient_accumulation_steps 1 \ ``` ## Intended Use and Limitations The model is finetuned to solve programming problems given a text description and optional starter code. ### How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-code-clippy-1.3B-apps") tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-code-clippy-1.3B-apps") prompt = """ A function to greet user. Given a user name it should say hello def greet(name): ANSWER: """ input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device) start = input_ids.size(1) out = model.generate(input_ids, do_sample=True, max_length=50, num_beams=2, early_stopping=True, eos_token_id=tokenizer.eos_token_id, ) print(tokenizer.decode(out[0][start:])) ``` ### Limitations and Biases The model is intended to be used for research purposes and comes with no guarantees of quality of generated code. The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discuss are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**. 1. **Over-reliance:** This model may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using this language model. 2. **Economic and labor market impacts:** Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [O*NET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software. 5. **Biases:** The model is trained on data containing prompt questions formatted in specific way. The performance of the model can be worse if the prompt formatting is different from that used in APPS dataset. GPT-CC is finetuned GPT-Neo and might have inhereted biases and limitations from it. See [GPT-Neo model card](https://huggingface.co/EleutherAI/gpt-neo-125M#limitations-and-biases) for details. ## Eval results Coming soon...

251281f4f3f24aab72ae032fd8510187

Sushant45/Canadian_Armed_Forces-clustered

Sushant45

distilbert

transformers

question-answering

mit

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Sushant45/Canadian_Armed_Forces-clustered This model is a fine-tuned version of [nandysoham16/0-clustered_aug](https://huggingface.co/nandysoham16/0-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5757 - Train End Logits Accuracy: 0.8542 - Train Start Logits Accuracy: 0.8160 - Validation Loss: 0.4930 - Validation End Logits Accuracy: 1.0 - Validation Start Logits Accuracy: 0.4000 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.5757 | 0.8542 | 0.8160 | 0.4930 | 1.0 | 0.4000 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

68759fc95135ad275b6300da3db62019

cankeles/DPTNet_WHAMR_enhsingle_16k

cankeles

asteroid

audio-to-audio

cc-by-sa-4.0

['Libri1Mix', 'enh_single']

['asteroid', 'audio', 'DPTNet', 'audio-to-audio']

## Asteroid model `cankeles/DPTNet_WHAMR_enhsignle_16k` Description: This model was trained by M. Can Keleş using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the `enh_single` task of the Libri1Mix dataset. Training config: ```yml data: mode: min nondefault_nsrc: null sample_rate: 16000 segment: 2.0 task: enh_single train_dir: wav16k/min/tr/ valid_dir: wav16k/min/cv/ filterbank: kernel_size: 16 n_filters: 64 stride: 8 main_args: exp_dir: exp/tmp help: null masknet: bidirectional: true chunk_size: 100 dropout: 0 ff_activation: relu ff_hid: 256 hop_size: 50 in_chan: 64 mask_act: sigmoid n_repeats: 2 n_src: 1 norm_type: gLN out_chan: 64 optim: lr: 0.001 optimizer: adam weight_decay: 1.0e-05 positional arguments: {} scheduler: d_model: 64 steps_per_epoch: 10000 training: batch_size: 4 early_stop: true epochs: 60 gradient_clipping: 5 half_lr: true num_workers: 4 ``` Results: On custom min test set : ```yml 'sar': 12.853384266251018, 'sar_imp': 8.950332361953906, 'sdr': 12.853384266251018, 'sdr_imp': 8.950332361953906, 'si_sdr': 12.247012621312548, 'si_sdr_imp': 8.429646186633407, 'sir': inf, 'sir_imp': nan, 'stoi': 0.9022338865380519, 'stoi_imp': 0.09735707619500522 ```

0a8718a840c9477ccf30de8a56e1c8a4

wietsedv/xlm-roberta-base-ft-udpos28-hyw

wietsedv

xlm-roberta

transformers

token-classification

apache-2.0

['hyw']

['universal_dependencies']

['part-of-speech', 'token-classification']

# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Western Armenian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hyw") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hyw") ```

92ebb7758c347d46c212592b59145783

emmyapi/distilbart-podimo-data-eval-3

emmyapi

bart

transformers

text2text-generation

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbart-podimo-data-eval-3 This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3828 - Rouge1: 32.8203 - Rouge2: 7.8994 - Rougel: 18.9659 - Rougelsum: 29.4196 - Gen Len: 114.5264 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:| | 3.9049 | 1.0 | 132 | 3.5343 | 30.2542 | 6.031 | 17.269 | 26.9847 | 113.7689 | | 3.4248 | 2.0 | 264 | 3.4055 | 31.6518 | 7.2786 | 18.2641 | 28.4006 | 114.6547 | | 3.1594 | 3.0 | 396 | 3.3579 | 32.0442 | 7.3554 | 18.3492 | 28.7615 | 113.7443 | | 2.9645 | 4.0 | 528 | 3.3445 | 32.0945 | 7.637 | 18.6289 | 28.899 | 115.5321 | | 2.8073 | 5.0 | 660 | 3.3470 | 32.7852 | 7.9597 | 19.2358 | 29.5057 | 108.3519 | | 2.685 | 6.0 | 792 | 3.3532 | 32.3775 | 7.661 | 18.6719 | 28.9282 | 117.1104 | | 2.5941 | 7.0 | 924 | 3.3711 | 32.6976 | 7.8917 | 19.069 | 29.3785 | 113.1943 | | 2.5267 | 8.0 | 1056 | 3.3828 | 32.8203 | 7.8994 | 18.9659 | 29.4196 | 114.5264 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.11.0 - Datasets 2.2.1 - Tokenizers 0.12.1

72b1cca90d88c8c60dc2c213298ad133

rajistics/donut-base-sroiev2

rajistics

vision-encoder-decoder

transformers

mit

['imagefolder']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # donut-base-sroiev2 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

53163a091a88e6264f3e9ac001b62b46

jarvisx17/wav2vec2-base-timit-small

jarvisx17

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-small This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5361 - Wer: 0.3380 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.571 | 1.0 | 500 | 1.9252 | 1.0022 | | 0.8969 | 2.01 | 1000 | 0.5066 | 0.5292 | | 0.4326 | 3.01 | 1500 | 0.4523 | 0.4562 | | 0.2993 | 4.02 | 2000 | 0.4228 | 0.4202 | | 0.2335 | 5.02 | 2500 | 0.4252 | 0.4178 | | 0.2009 | 6.02 | 3000 | 0.4136 | 0.3910 | | 0.1552 | 7.03 | 3500 | 0.4747 | 0.3863 | | 0.1388 | 8.03 | 4000 | 0.4359 | 0.3859 | | 0.1226 | 9.04 | 4500 | 0.4367 | 0.3879 | | 0.1109 | 10.04 | 5000 | 0.4360 | 0.3760 | | 0.0991 | 11.04 | 5500 | 0.4899 | 0.3672 | | 0.0882 | 12.05 | 6000 | 0.4608 | 0.3653 | | 0.0792 | 13.05 | 6500 | 0.4882 | 0.3703 | | 0.0745 | 14.06 | 7000 | 0.4716 | 0.3625 | | 0.065 | 15.06 | 7500 | 0.4896 | 0.3651 | | 0.0596 | 16.06 | 8000 | 0.4831 | 0.3659 | | 0.0563 | 17.07 | 8500 | 0.5092 | 0.3585 | | 0.0536 | 18.07 | 9000 | 0.5376 | 0.3675 | | 0.0465 | 19.08 | 9500 | 0.5019 | 0.3534 | | 0.049 | 20.08 | 10000 | 0.4869 | 0.3723 | | 0.0423 | 21.08 | 10500 | 0.4947 | 0.3501 | | 0.0348 | 22.09 | 11000 | 0.5524 | 0.3453 | | 0.0315 | 23.09 | 11500 | 0.5369 | 0.3499 | | 0.0312 | 24.1 | 12000 | 0.5283 | 0.3519 | | 0.0258 | 25.1 | 12500 | 0.5202 | 0.3461 | | 0.0249 | 26.1 | 13000 | 0.5270 | 0.3449 | | 0.0236 | 27.11 | 13500 | 0.5388 | 0.3408 | | 0.0206 | 28.11 | 14000 | 0.5361 | 0.3388 | | 0.0224 | 29.12 | 14500 | 0.5361 | 0.3380 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.13.1+cu116 - Datasets 1.18.3 - Tokenizers 0.13.2

0dc34a9ff135d598509d460aa446ffb7

johko/capdec_025

johko

image-to-text

apache-2.0

['en']

['MS-COCO', 'Flickr30k']

['Image Captioning']

# CapDec - NoiseLevel: 0.025 ## Model Description These are model weights originally provided by the authors of the paper [Text-Only Training for Image Captioning using Noise-Injected CLIP](https://arxiv.org/pdf/2211.00575.pdf). Their method aims to train CLIP with only text samples. Therefore they are injecting zero-mean Gaussian Noise into the text embeddings before decoding. In their words: *Specifically, we assume that the visual embedding corresponding to a text embedding lies somewhere within a ball of small radius around the text embedding (see Fig. 1). We would like all text embeddings in this ball to decode to the same caption,which should also correspond to the visual content mapped to this ball. We implement this intuition by adding zero-mean Gaussian noise of STD to the text embedding before decoding it.* The "Noise Level" of 0.025 is equivalent to the Noise Variance which is the square of the STD. The reported metrics are results of a model with a Noise Variance of 0.016, which the authors unfortunately do not provide in their repository. ## Datasets The authors trained the model on MS-COCO and Flickr30k datasets. ## Performance The authors don't explicitly report the performance for this NoiseLevel but it can be estimated from the following figure from the original paper: 

98c3c1dca52a704b8370370e0a94108d

JovialValley/model_broadclass_onSet1

JovialValley

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_broadclass_onSet1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9014 - 0 Precision: 0.5217 - 0 Recall: 1.0 - 0 F1-score: 0.6857 - 0 Support: 24 - 1 Precision: 1.0 - 1 Recall: 0.7692 - 1 F1-score: 0.8696 - 1 Support: 39 - 2 Precision: 1.0 - 2 Recall: 0.5652 - 2 F1-score: 0.7222 - 2 Support: 23 - 3 Precision: 1.0 - 3 Recall: 0.75 - 3 F1-score: 0.8571 - 3 Support: 12 - Accuracy: 0.7755 - Macro avg Precision: 0.8804 - Macro avg Recall: 0.7711 - Macro avg F1-score: 0.7837 - Macro avg Support: 98 - Weighted avg Precision: 0.8829 - Weighted avg Recall: 0.7755 - Weighted avg F1-score: 0.7884 - Weighted avg Support: 98 - Wer: 0.9368 - Mtrix: [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 9, 30, 0, 0], [2, 10, 0, 13, 0], [3, 3, 0, 0, 9]] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 70 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support | Wer | Mtrix | |:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:---------------------------------------------------------------------------------------:| | 2.395 | 4.16 | 100 | 2.2004 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 2.2919 | 8.33 | 200 | 2.1576 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 2.0987 | 12.49 | 300 | 2.0882 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.9079 | 16.65 | 400 | 1.8619 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.7168 | 20.82 | 500 | 1.6469 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.551 | 24.98 | 600 | 1.6614 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.6399 | 29.16 | 700 | 1.5818 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.3329 | 33.33 | 800 | 1.2267 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.1996 | 37.49 | 900 | 1.2143 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.01 | 41.65 | 1000 | 0.9496 | 0.2474 | 1.0 | 0.3967 | 24 | 1.0 | 0.0256 | 0.05 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2551 | 0.3119 | 0.2564 | 0.1117 | 98 | 0.4586 | 0.2551 | 0.1170 | 98 | 0.9777 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 38, 1, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 0.9516 | 45.82 | 1100 | 0.9471 | 0.2927 | 1.0 | 0.4528 | 24 | 1.0 | 0.3846 | 0.5556 | 39 | 1.0 | 0.0435 | 0.0833 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.4082 | 0.5732 | 0.3570 | 0.2729 | 98 | 0.7043 | 0.4082 | 0.3515 | 98 | 0.9661 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 24, 15, 0, 0], [2, 22, 0, 1, 0], [3, 12, 0, 0, 0]] | | 0.9544 | 49.98 | 1200 | 0.9452 | 0.3582 | 1.0 | 0.5275 | 24 | 1.0 | 0.5128 | 0.6780 | 39 | 1.0 | 0.3043 | 0.4667 | 23 | 0.75 | 0.25 | 0.375 | 12 | 0.5510 | 0.7771 | 0.5168 | 0.5118 | 98 | 0.8122 | 0.5510 | 0.5544 | 98 | 0.9540 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 18, 20, 0, 1], [2, 16, 0, 7, 0], [3, 9, 0, 0, 3]] | | 0.9538 | 54.16 | 1300 | 0.9259 | 0.4615 | 1.0 | 0.6316 | 24 | 1.0 | 0.6923 | 0.8182 | 39 | 1.0 | 0.5217 | 0.6857 | 23 | 0.8571 | 0.5 | 0.6316 | 12 | 0.7041 | 0.8297 | 0.6785 | 0.6918 | 98 | 0.8506 | 0.7041 | 0.7185 | 98 | 0.9439 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 11, 27, 0, 1], [2, 11, 0, 12, 0], [3, 6, 0, 0, 6]] | | 0.952 | 58.33 | 1400 | 0.9052 | 0.4528 | 1.0 | 0.6234 | 24 | 1.0 | 0.6667 | 0.8 | 39 | 1.0 | 0.4348 | 0.6061 | 23 | 0.8889 | 0.6667 | 0.7619 | 12 | 0.6939 | 0.8354 | 0.6920 | 0.6978 | 98 | 0.8524 | 0.6939 | 0.7066 | 98 | 0.9464 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 12, 26, 0, 1], [2, 13, 0, 10, 0], [3, 4, 0, 0, 8]] | | 0.8938 | 62.49 | 1500 | 0.9070 | 0.48 | 1.0 | 0.6486 | 24 | 0.9677 | 0.7692 | 0.8571 | 39 | 1.0 | 0.4348 | 0.6061 | 23 | 1.0 | 0.5833 | 0.7368 | 12 | 0.7245 | 0.8619 | 0.6968 | 0.7122 | 98 | 0.8598 | 0.7245 | 0.7324 | 98 | 0.9398 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 9, 30, 0, 0], [2, 12, 1, 10, 0], [3, 5, 0, 0, 7]] | | 0.9027 | 66.65 | 1600 | 0.8919 | 0.5714 | 1.0 | 0.7273 | 24 | 1.0 | 0.8462 | 0.9167 | 39 | 1.0 | 0.7391 | 0.85 | 23 | 1.0 | 0.5 | 0.6667 | 12 | 0.8163 | 0.8929 | 0.7713 | 0.7902 | 98 | 0.8950 | 0.8163 | 0.8240 | 98 | 0.9398 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 6, 33, 0, 0], [2, 6, 0, 17, 0], [3, 6, 0, 0, 6]] | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

a358b3250a42c7dc150a15a2437218b8

mariolinml/roberta_large-ner-conll2003_0818_v1

mariolinml

roberta

transformers

token-classification

mit

['conll2003']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta_large-ner-conll2003_0818_v1 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.1481 - Precision: 0.8993 - Recall: 0.9269 - F1: 0.9129 - Accuracy: 0.9784 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2033 | 1.0 | 878 | 0.0472 | 0.9277 | 0.9551 | 0.9412 | 0.9887 | | 0.044 | 2.0 | 1756 | 0.0428 | 0.9365 | 0.9610 | 0.9486 | 0.9895 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

ea60ea814e16a53d41e7393b7a8bc1b0

aXhyra/demo_hate_1234567

aXhyra

distilbert

transformers

text-classification

apache-2.0

['tweet_eval']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # demo_hate_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.8697 - F1: 0.7773 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.320702985778492e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 282 | 0.4850 | 0.7645 | | 0.3877 | 2.0 | 564 | 0.5160 | 0.7856 | | 0.3877 | 3.0 | 846 | 0.6927 | 0.7802 | | 0.1343 | 4.0 | 1128 | 0.8697 | 0.7773 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3

ac1685c5a7422439ced714d264808437

cataluna84/xlm-roberta-base-finetuned-panx-de-fr

cataluna84

xlm-roberta

transformers

token-classification

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1608 - F1: 0.8593 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2888 | 1.0 | 715 | 0.1779 | 0.8233 | | 0.1437 | 2.0 | 1430 | 0.1570 | 0.8497 | | 0.0931 | 3.0 | 2145 | 0.1608 | 0.8593 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

924504ad4da58d47ae22bb170de9f040

HuggingAlex1247/gelectra-large-germaner

HuggingAlex1247

electra

transformers

token-classification

mit

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # HuggingAlex1247/gelectra-large-germaner This model is a fine-tuned version of [deepset/gelectra-large](https://huggingface.co/deepset/gelectra-large) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1696 - Validation Loss: 0.0800 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3475, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1696 | 0.0800 | 0 | ### Framework versions - Transformers 4.22.2 - TensorFlow 2.6.2 - Datasets 1.18.0 - Tokenizers 0.12.1

1cbba9c71c758dec92e9f2d570c5d01d

jonatasgrosman/exp_w2v2t_it_vp-it_s411

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['it']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'it']

# exp_w2v2t_it_vp-it_s411 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

5f20bb60c4d8b6601c8fccee3708df93

sd-concepts-library/a-female-hero-from-the-legend-of-mir

sd-concepts-library

mit

[]

### a female hero from The Legend of Mir on Stable Diffusion This is the `a <female-hero> from The Legend of Mir` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:      

a8c32f1a9f0ed8b24e11419cd313cb94

jonatasgrosman/wav2vec2-xls-r-1b-italian

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['it']

['mozilla-foundation/common_voice_8_0']

['automatic-speech-recognition', 'hf-asr-leaderboard', 'it', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event']

# Fine-tuned XLS-R 1B model for speech recognition in Italian Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on Italian using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [Multilingual TEDx](http://www.openslr.org/100), [Multilingual LibriSpeech](https://www.openslr.org/94/), and [Voxpopuli](https://github.com/facebookresearch/voxpopuli). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool, and thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) ## Usage Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-xls-r-1b-italian") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = model.transcribe(audio_paths) ``` Writing your own inference script: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "it" MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-italian" SAMPLES = 10 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_sentences = processor.batch_decode(predicted_ids) ``` ## Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-italian --dataset mozilla-foundation/common_voice_8_0 --config it --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-italian --dataset speech-recognition-community-v2/dev_data --config it --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr-1b-italian, title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {I}talian}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-italian}}, year={2022} } ```

ae960af97aa23a706bd5680f71c55ff0

Helsinki-NLP/opus-mt-fr-bzs

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-fr-bzs * source languages: fr * target languages: bzs * OPUS readme: [fr-bzs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-bzs/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-bzs/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-bzs/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-bzs/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fr.bzs | 30.2 | 0.477 |

4057788f15cc524c7e58817be5dc085f

aipicasso/cool-japan-diffusion-2-1-1-beta

aipicasso

diffusers

text-to-image

other

['stable-diffusion', 'text-to-image']

# Cool Japan Diffusion 2.1.1 Beta Model Card  [注意事项。中国将对图像生成的人工智能实施法律限制。 ](http://www.cac.gov.cn/2022-12/11/c_1672221949318230.htm) （中国国内にいる人への警告） English version is [here](README_en.md). # はじめに Cool Japan Diffusion (for learning) はStable Diffsionをファインチューニングして、アニメやマンガ、ゲームなどのクールジャパンを表現することに特化したモデルです。なお、内閣府のクールジャパン戦略とは特に関係はありません。 # ライセンスについて ライセンスについては、もとのライセンス CreativeML Open RAIL++-M License に例外を除き商用利用禁止を追加しただけです。 例外を除き商用利用禁止を追加した理由は創作業界に悪影響を及ぼしかねないという懸念からです。 この懸念が払拭されれば、次のバージョンから元のライセンスに戻し、商用利用可能とします。 ちなみに、元のライセンスの日本語訳は[こちら](https://qiita.com/robitan/items/887d9f3153963114823d)になります。 営利企業にいる方は法務部にいる人と相談してください。 趣味で利用する方はあまり気にしなくても一般常識を守れば大丈夫なはずです。 なお、ライセンスにある通り、このモデルを改造しても、このライセンスを引き継ぐ必要があります。 # 法律や倫理について 本モデルは日本にて作成されました。したがって、日本の法律が適用されます。 本モデルの学習は、著作権法第30条の4に基づき、合法であると主張します。 また、本モデルの配布については、著作権法や刑法175条に照らしてみても、 正犯や幇助犯にも該当しないと主張します。詳しくは柿沼弁護士の[見解](https://twitter.com/tka0120/status/1601483633436393473?s=20&t=yvM9EX0Em-_7lh8NJln3IQ)を御覧ください。 ただし、ライセンスにもある通り、本モデルの生成物は各種法令に従って取り扱って下さい。 しかし、本モデルを配布する行為が倫理的に良くないとは作者は思っています。 これは学習する著作物に対して著作者の許可を得ていないためです。 ただし、学習するには著作者の許可は法律上必要もなく、検索エンジンと同様法律上は問題はありません。 したがって、法的な側面ではなく、倫理的な側面を調査する目的も本配布は兼ねていると考えてください。 # 使い方 手軽に楽しみたい方は、こちらの[Space](https://huggingface.co/spaces/aipicasso/cool-japan-diffusion-latest-demo)をお使いください。 詳しい本モデルの取り扱い方は[こちらの取扱説明書](https://alfredplpl.hatenablog.com/entry/2023/01/11/182146)にかかれています。 モデルは[ここ](https://huggingface.co/aipicasso/cool-japan-diffusion-2-1-1-beta/resolve/main/v2-1-1-beta.ckpt)からダウンロードできます。 以下、一般的なモデルカードの日本語訳です。 ## モデル詳細 - **開発者:** Robin Rombach, Patrick Esser, Alfred Increment - **モデルタイプ:** 拡散モデルベースの text-to-image 生成モデル - **言語:** 日本語 - **ライセンス:** CreativeML Open RAIL++-M-NC License - **モデルの説明:** このモデルはプロンプトに応じて適切な画像を生成することができます。アルゴリズムは [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) と [OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip) です。 - **補足:** - **参考文献:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## モデルの使用例 Stable Diffusion v2と同じ使い方です。 たくさんの方法がありますが、２つのパターンを提供します。 - Web UI - Diffusers ### Web UIの場合 こちらの[取扱説明書](https://alfredplpl.hatenablog.com/entry/2023/01/11/182146)に従って作成してください。 ### Diffusersの場合 [🤗's Diffusers library](https://github.com/huggingface/diffusers) を使ってください。 まずは、以下のスクリプトを実行し、ライブラリをいれてください。 ```bash pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy ``` 次のスクリプトを実行し、画像を生成してください。 ```python from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler import torch model_id = "aipicasso/cool-japan-diffusion-2-1-1-beta" scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)#,use_auth_token="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") pipe = pipe.to("cuda") prompt = "anime, a portrait of a girl with black short hair and red eyes, kimono, full color illustration, official art, 4k, detailed" negative_prompt="(((deformed))), blurry, ((((bad anatomy)))), bad pupil, disfigured, poorly drawn face, mutation, mutated, (extra limb), (ugly), (poorly drawn hands), bad hands, fused fingers, messy drawing, broken legs censor, low quality, ((mutated hands and fingers:1.5), (long body :1.3), (mutation, poorly drawn :1.2), ((bad eyes)), ui, error, missing fingers, fused fingers, one hand with more than 5 fingers, one hand with less than 5 fingers, one hand with more than 5 digit, one hand with less than 5 digit, extra digit, fewer digits, fused digit, missing digit, bad digit, liquid digit, long body, uncoordinated body, unnatural body, lowres, jpeg artifacts, 2d, 3d, cg, text" image = pipe(prompt,negative_prompt=negative_prompt, width=512, height=512, num_inference_steps=20).images[0] image.save("girl.png") ``` **注意**: - [xformers](https://github.com/facebookresearch/xformers) を使うと早くなるらしいです。 - GPUを使う際にGPUのメモリが少ない人は `pipe.enable_attention_slicing()` を使ってください。 #### 想定される用途 - コンテスト - [AIアートグランプリ](https://www.aiartgrandprix.com/)への投稿 - ファインチューニングに用いた全データを開示し、審査基準を満たしていることを判断してもらうようにします。 - コンテストに向けて、要望があれば、Hugging Face の Community などで私に伝えてください。 - 画像生成AIに関する報道 - 公共放送だけでなく、営利企業でも可能 - 画像合成AIに関する情報を「知る権利」は創作業界に悪影響を及ぼさないと判断したためです。また、報道の自由などを尊重しました。 - クールジャパンの紹介 - 他国の人にクールジャパンとはなにかを説明すること。 - 他国の留学生はクールジャパンに惹かれて日本に来ることがおおくあります。そこで、クールジャパンが日本では「クールでない」とされていることにがっかりされることがとても多いとAlfred Incrementは感じております。他国の人が憧れる自国の文化をもっと誇りに思ってください。 - 研究開発 - Discord上でのモデルの利用 - プロンプトエンジニアリング - ファインチューニング（追加学習とも） - DreamBooth など - 他のモデルとのマージ - Latent Diffusion Modelとクールジャパンとの相性 - 本モデルの性能をFIDなどで調べること - 本モデルがStable Diffusion以外のモデルとは独立であることをチェックサムやハッシュ関数などで調べること - 教育 - 美大生や専門学校生の卒業制作 - 大学生の卒業論文や課題制作 - 先生が画像生成AIの現状を伝えること - 自己表現 - SNS上で自分の感情や思考を表現すること - Hugging Face の Community にかいてある用途 - 日本語か英語で質問してください #### 想定されない用途 - 物事を事実として表現するようなこと - 収益化されているYouTubeなどのコンテンツへの使用 - 商用のサービスとして直接提供すること - 先生を困らせるようなこと - その他、創作業界に悪影響を及ぼすこと # 使用してはいけない用途や悪意のある用途 - デジタル贋作 ([Digital Forgery](https://arxiv.org/abs/2212.03860)) は公開しないでください（著作権法に違反するおそれ） - 特に既存のキャラクターは公開しないでください（著作権法に違反するおそれ） - なお、学習していない[キャラクターも生成できる](https://twitter.com/ThePioneerJPnew/status/1609074173892235264?s=20&t=-rY1ufzNeIDT3Fm5YdME6g)そうです。（このツイート自体は研究目的として許可しています。） - 他人の作品を無断でImage-to-Imageしないでください（著作権法に違反するおそれ） - わいせつ物を頒布しないでください (刑法175条に違反するおそれ） - いわゆる業界のマナーを守らないようなこと - 事実に基づかないことを事実のように語らないようにしてください（威力業務妨害罪が適用されるおそれ） - フェイクニュース ## モデルの限界やバイアス ### モデルの限界 - よくわかっていない ### バイアス Stable Diffusionと同じバイアスが掛かっています。 気をつけてください。 ## 学習 **学習データ** 次のデータを主に使ってStable Diffusionをファインチューニングしています。 - VAEについて - Danbooruなどの無断転載サイトを除いた日本の国内法を遵守したデータ: 60万種類 （データ拡張により無限枚作成） - U-Netについて - Danbooruなどの無断転載サイトを除いた日本の国内法を遵守したデータ: 80万ペア **学習プロセス** Stable DiffusionのVAEとU-Netをファインチューニングしました。 - **ハードウェア:** RTX 3090 - **オプティマイザー:** AdamW - **Gradient Accumulations**: 1 - **バッチサイズ:** 1 ## 評価結果 ## 環境への影響 ほとんどありません。 - **ハードウェアタイプ:** RTX 3090 - **使用時間（単位は時間）:** 500 - **クラウド事業者:** なし - **学習した場所:** 日本 - **カーボン排出量:** そんなにない ## 参考文献 @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *このモデルカードは [Stable Diffusion v2](https://huggingface.co/stabilityai/stable-diffusion-2/raw/main/README.md) に基づいて、Alfred Incrementがかきました。

128612d4fd5b2b28bac1f818fbffc7bb

DOOGLAK/Article_100v7_NER_Model_3Epochs_UNAUGMENTED

DOOGLAK

bert

transformers

token-classification

apache-2.0

['article100v7_wikigold_split']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Article_100v7_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v7_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.6011 - Precision: 0.1661 - Recall: 0.0138 - F1: 0.0254 - Accuracy: 0.7860 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 12 | 0.7375 | 0.0 | 0.0 | 0.0 | 0.7810 | | No log | 2.0 | 24 | 0.6356 | 0.0571 | 0.0010 | 0.0020 | 0.7820 | | No log | 3.0 | 36 | 0.6011 | 0.1661 | 0.0138 | 0.0254 | 0.7860 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6

45a69054538a670287f756a212b421b0

SummerZhang/distilbert-base-uncased-finetuned-squad

SummerZhang

distilbert

transformers

question-answering

apache-2.0

['squad_v2']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.2293 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.248 | 1.0 | 8235 | 1.2293 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

4f22353604ecba850f196094655a5d0f

clp/vit-base-patch16-224-finetuned

clp

vit

transformers

image-classification

apache-2.0

['imagefolder']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7617 - Accuracy: 0.3333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.6063 | 0.6667 | | No log | 2.0 | 2 | 0.6958 | 0.3333 | | No log | 3.0 | 3 | 0.7617 | 0.3333 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

2c010138d535421ded164f5c002d62f0

Yuri/xlm-roberta-base-finetuned-panx-de

Yuri

xlm-roberta

transformers

token-classification

mit

['xtreme']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1365 - F1: 0.8649 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 | | 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 | | 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

f293cf457c742fd4dfac4044c29cf151

chrisvinsen/wav2vec2-15

chrisvinsen

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-15 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8623 - Wer: 0.8585 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.6808 | 1.37 | 200 | 3.7154 | 1.0 | | 3.0784 | 2.74 | 400 | 3.1542 | 1.0 | | 2.8919 | 4.11 | 600 | 2.9918 | 1.0 | | 2.8317 | 5.48 | 800 | 2.8971 | 1.0 | | 2.7958 | 6.85 | 1000 | 2.8409 | 1.0 | | 2.7699 | 8.22 | 1200 | 2.8278 | 1.0 | | 2.6365 | 9.59 | 1400 | 2.4657 | 1.0 | | 2.1096 | 10.96 | 1600 | 1.8358 | 0.9988 | | 1.6485 | 12.33 | 1800 | 1.4525 | 0.9847 | | 1.3967 | 13.7 | 2000 | 1.2467 | 0.9532 | | 1.2492 | 15.07 | 2200 | 1.1261 | 0.9376 | | 1.1543 | 16.44 | 2400 | 1.0654 | 0.9194 | | 1.0863 | 17.81 | 2600 | 1.0136 | 0.9161 | | 1.0275 | 19.18 | 2800 | 0.9601 | 0.8827 | | 0.9854 | 20.55 | 3000 | 0.9435 | 0.8878 | | 0.9528 | 21.92 | 3200 | 0.9170 | 0.8807 | | 0.926 | 23.29 | 3400 | 0.9121 | 0.8783 | | 0.9025 | 24.66 | 3600 | 0.8884 | 0.8646 | | 0.8909 | 26.03 | 3800 | 0.8836 | 0.8690 | | 0.8717 | 27.4 | 4000 | 0.8810 | 0.8646 | | 0.8661 | 28.77 | 4200 | 0.8623 | 0.8585 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

71746766eb5f78afa54b73d4061cf224

Gabriel/bart-base-cnn-swe

Gabriel

bart

transformers

summarization

mit

['sv']

['Gabriel/cnn_daily_swe']

{'emissions': 0.0334, 'source': 'Google Colab', 'training_type': 'fine-tuning', 'geographical_location': 'Fredericia, Denmark', 'hardware_used': 'Tesla P100-PCIE-16GB'}

['summarization']

# bart-base-cnn-swe This model is a W.I.P ## Model description BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. This model is a fine-tuned version of [KBLab/bart-base-swedish-cased](https://huggingface.co/KBLab/bart-base-swedish-cased) on the [Gabriel/bart-base-cnn-swe](https://huggingface.co/datasets/Gabriel/cnn_daily_swe) dataset and can be used for summarization tasks. ## Intended uses & limitations This model should only be used to fine-tune further on and summarization tasks. ```python from transformers import pipeline summarizer = pipeline("summarization", model="Gabriel/bart-base-cnn-swe") ARTICLE = """ Frankrike lås Sebastien Chabal har nämnts för en farlig tackling på Englands Simon Shaw under lördagens VM semifinal i Paris. Simon Shaw lastar av trots att Raphael Ibanez, vänster, och Sebastien Chabal. Sale Sharks framåt kommer att ställas inför en disciplinär utfrågning på måndag efter hans tackling på motsatt andra-rower Shaw noterades genom att citera kommissionär Dennis Wheelahan. Chabal började matchen på ersättningsbänken, men kom i 26: e minuten att ersätta den skadade Fabien Pelous under värd Frankrikes 14-9 nederlag. Om han blir avstängd missar Chabal fredagens tredje och fjärde match på Parc des Princes. Samtidigt, Frankrike tränare Bernard Laporte sade att nederlaget var svårare att ta än Englands 24-7 seger i 2003 semifinalen. "År 2003 var de bättre än oss. I själva verket var de bättre än alla", sade Laporte, som lämnar sin roll att tillträda posten som junior idrottsminister i den franska regeringen. "De var som Nya Zeeland i denna turnering - favoriten, förutom att de gick hela vägen. Den här gången är det svårare för igår var det 50-50." Samtidigt, England -- försöker bli den första nationen att försvara VM-titeln -- avslöjade att stjärna kicker Jonny Wilkinson återigen hade problem med matchbollarna under semifinalen. Flughalvan, som uttryckte sin oro efter att ha kämpat med stöveln mot Australien, avvisade en boll innan han sparkade en vital trepoängare mot Frankrike. "Vi sa det inte förra veckan men en icke-match bollen kom ut på fältet i Marseille som Jonny sparkade," chef för rugby Rob Andrew sade. "Han tänkte inte på det när han sparkade det. Matchbollarna är märkta, numrerade ett till sex. Igår kväll hade de "World Cup semifinal England vs Frankrike" skrivet på dem. På matchkvällen var Jonny vaksam när han sparkade för mål att de faktiskt var matchbollar han sparkade. "Träningsbollarna förlorar tryck och form. Hela frågan förra veckan, arrangörerna accepterade alla sex matchbollar bör användas av båda sidor på torsdagen före matchen. " E-post till en vän. """ print(summarizer(ARTICLE, max_length=130, min_length=30, num_beams=10 ,do_sample=False)) >>> [{'summary_text': """ Frankrike lås Sebastien Chabal har nämnts för en farlig tackling på Englands Simon Shaw under VM semifinal i Paris. Sale Sharks framåt kommer att ställas inför en disciplinär utfrågning på måndag efter hans tackling på motsatt andra - rower Shaw noterades genom att citera kommissionär Dennis Wheelahan. Om Chabal blir avstängd missar Chabal fredagens tredje och fjärde match på Parc des Princes."""}] ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2*2 = 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.2349 | 1.0 | 17944 | 2.0643 | 21.9564 | 10.2133 | 17.9958 | 20.6502 | 19.9992 | | 2.0726 | 2.0 | 35888 | 2.0253 | 22.0568 | 10.3302 | 18.0648 | 20.7482 | 19.9996 | | 1.8658 | 3.0 | 53832 | 2.0333 | 22.0871 | 10.2902 | 18.0577 | 20.7082 | 19.998 | | 1.8121 | 4.0 | 71776 | 1.9759 | 22.2046 | 10.4332 | 18.1753 | 20.846 | 19.9971 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

6ea19655482812f44ac5593f6b66fe37

hiiamsid/BETO_es_binary_classification

hiiamsid

bert

transformers

text-classification

apache-2.0

['es']

['self made to classify whether text is related to technology or not.']

['es', 'ticket classification']

# BETO(cased) This model was built using pytorch. ## Model description Input for the model: Any spanish text Output for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate)) #### How to use Here is how to use this model to get the features of a given text in *PyTorch*: ```python # You can include sample code which will be formatted from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hiiamsid/BETO_es_binary_classification") model = AutoModelForSequenceClassification.from_pretrained("hiiamsid/BETO_es_binary_classification") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ## Training procedure I trained on the dataset on the [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased).

c0796c1e7e6ddef709aa47bf6978c39f

MichaelCHomeX/distilbert-base-uncased-finetuned-imdb

MichaelCHomeX

distilbert

transformers

fill-mask

apache-2.0

['imdb']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4721 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7086 | 1.0 | 157 | 2.4898 | | 2.5796 | 2.0 | 314 | 2.4230 | | 2.5269 | 3.0 | 471 | 2.4354 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

9c960819ac12a41385c34a9928e1818a

Helsinki-NLP/opus-mt-lue-sv

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-lue-sv * source languages: lue * target languages: sv * OPUS readme: [lue-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lue-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lue-sv/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lue-sv/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lue-sv/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.lue.sv | 23.7 | 0.412 |

374430b2d9c55913259feb666ef2bf1b

eslamxm/mt5-base-finetuned-en-cnn

eslamxm

mt5

transformers

summarization

apache-2.0

['cnn_dailymail']

['summarization', 'en', 'mt5', 'Abstractive Summarization', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-base-finetuned-en-cnn This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 3.1286 - Rouge-1: 22.84 - Rouge-2: 10.11 - Rouge-l: 21.8 - Gen Len: 19.0 - Bertscore: 87.12 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 5 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

ccf87daaab582ca64c4b9e62cb3f6dab

anas-awadalla/t5-base-few-shot-k-64-finetuned-squad-infilling-seed-4

anas-awadalla

t5

transformers

text2text-generation

apache-2.0

['squad']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-few-shot-k-64-finetuned-squad-infilling-seed-4 This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

a032ba6168e4ccd0afb4954f4eb1dd16

MultiBertGunjanPatrick/multiberts-seed-4-80k

MultiBertGunjanPatrick

bert

transformers

apache-2.0

['en']

['bookcorpus', 'wikipedia']

['exbert', 'multiberts', 'multiberts-seed-4']

# MultiBERTs Seed 4 Checkpoint 80k (uncased) Seed 4 intermediate checkpoint 80k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-4](https://hf.co/multberts-seed-4). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). ## Model description MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MultiBERTs model as inputs. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-4-80k') model = BertModel.from_pretrained("multiberts-seed-4-80k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint. ## Training data The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size of 256. The sequence length was set to 512 throughout. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-16163, author = {Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis}, journal = {CoRR}, volume = {abs/2106.16163}, year = {2021}, url = {https://arxiv.org/abs/2106.16163}, eprinttype = {arXiv}, eprint = {2106.16163}, timestamp = {Mon, 05 Jul 2021 15:15:50 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=multiberts"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

4ce124bc741d1ae75dde73c3de397f82

Suya03/my_awesome_billsum_model

Suya03

t5

transformers

text2text-generation

apache-2.0

['billsum']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5312 - Rouge1: 0.1421 - Rouge2: 0.0515 - Rougel: 0.1184 - Rougelsum: 0.1182 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.8298 | 0.1269 | 0.0364 | 0.1068 | 0.1068 | 19.0 | | No log | 2.0 | 124 | 2.6134 | 0.133 | 0.045 | 0.1114 | 0.1109 | 19.0 | | No log | 3.0 | 186 | 2.5476 | 0.142 | 0.0518 | 0.118 | 0.1179 | 19.0 | | No log | 4.0 | 248 | 2.5312 | 0.1421 | 0.0515 | 0.1184 | 0.1182 | 19.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

16fe2c675025318a10f7c18de86252ed

rossanez/t5-small-finetuned-de-en-256-wd-01

rossanez

t5

transformers

text2text-generation

apache-2.0

['wmt14']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-de-en-256-wd-01 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 188 | 2.1202 | 7.5964 | 17.3996 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

b8b02b90d4ebd02f3dabfe81cb7931a2

leonadase/distilbert-base-uncased-finetuned-ner

leonadase

distilbert

transformers

token-classification

apache-2.0

['conll2003']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0611 - Precision: 0.9210 - Recall: 0.9357 - F1: 0.9283 - Accuracy: 0.9832 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2341 | 1.0 | 878 | 0.0734 | 0.9118 | 0.9206 | 0.9162 | 0.9799 | | 0.0546 | 2.0 | 1756 | 0.0591 | 0.9210 | 0.9350 | 0.9279 | 0.9829 | | 0.0297 | 3.0 | 2634 | 0.0611 | 0.9210 | 0.9357 | 0.9283 | 0.9832 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

90cd0e08a55b66d816cf64aa2ef4406e

yanaiela/roberta-base-epoch_21

yanaiela

roberta

transformers

fill-mask

mit

['en']

['wikipedia', 'bookcorpus']

['roberta-base', 'roberta-base-epoch_21']

# RoBERTa, Intermediate Checkpoint - Epoch 21 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly initialized weights before the training) to provide the ability to study the training dynamics of such models, and other possible use-cases. These models were trained in part of a work that studies how simple statistics from data, such as co-occurrences affects model predictions, which are described in the paper [Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251). This is RoBERTa-base epoch_21. ## Model Description This model was captured during a reproduction of [RoBERTa-base](https://huggingface.co/roberta-base), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM). The intended uses, limitations, training data and training procedure for the fully trained model are similar to [RoBERTa-base](https://huggingface.co/roberta-base). Two major differences with the original model: * We trained our model for 100K steps, instead of 500K * We only use Wikipedia and the Book Corpus, as corpora which are publicly available. ### How to use Using code from [RoBERTa-base](https://huggingface.co/roberta-base), here is an example based on PyTorch: ``` from transformers import pipeline model = pipeline("fill-mask", model='yanaiela/roberta-base-epoch_83', device=-1, top_k=10) model("Hello, I'm the <mask> RoBERTa-base language model") ``` ## Citation info ```bibtex @article{2207.14251, Author = {Yanai Elazar and Nora Kassner and Shauli Ravfogel and Amir Feder and Abhilasha Ravichander and Marius Mosbach and Yonatan Belinkov and Hinrich Schütze and Yoav Goldberg}, Title = {Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions}, Year = {2022}, Eprint = {arXiv:2207.14251}, } ```

e89f68a90e32894fa255fcc1af61f64b

dxiao/bert-finetuned-ner

dxiao

bert

transformers

token-classification

apache-2.0

['conll2003']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0583 - Precision: 0.9396 - Recall: 0.9530 - F1: 0.9463 - Accuracy: 0.9868 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0883 | 1.0 | 1756 | 0.0702 | 0.9184 | 0.9320 | 0.9252 | 0.9819 | | 0.0338 | 2.0 | 3512 | 0.0661 | 0.9263 | 0.9480 | 0.9370 | 0.9853 | | 0.0174 | 3.0 | 5268 | 0.0583 | 0.9396 | 0.9530 | 0.9463 | 0.9868 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

1778afb0eb36cc68d90613cb29fafcaa

Helsinki-NLP/opus-mt-tc-big-fa-itc

Helsinki-NLP

marian

transformers

translation

cc-by-4.0

['fa', 'fr', 'pt', 'ro']

['translation', 'opus-mt-tc']

# opus-mt-tc-big-fa-itc ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [How to Get Started With the Model](#how-to-get-started-with-the-model) - [Training](#training) - [Evaluation](#evaluation) - [Citation Information](#citation-information) - [Acknowledgements](#acknowledgements) ## Model Details Neural machine translation model for translating from Persian (fa) to Italic languages (itc). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation (transformer-big) - **Release**: 2022-07-23 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): fas - Target Language(s): fra ita por ron spa - Language Pair(s): fas-fra fas-por fas-ron - Valid Target Language Labels: >>acf<< >>aoa<< >>arg<< >>ast<< >>cat<< >>cbk<< >>ccd<< >>cks<< >>cos<< >>cri<< >>crs<< >>dlm<< >>drc<< >>egl<< >>ext<< >>fab<< >>fax<< >>fra<< >>frc<< >>frm<< >>fro<< >>frp<< >>fur<< >>gcf<< >>gcr<< >>glg<< >>hat<< >>idb<< >>ist<< >>ita<< >>itk<< >>kea<< >>kmv<< >>lad<< >>lad_Latn<< >>lat<< >>lat_Latn<< >>lij<< >>lld<< >>lmo<< >>lou<< >>mcm<< >>mfe<< >>mol<< >>mwl<< >>mxi<< >>mzs<< >>nap<< >>nrf<< >>oci<< >>osc<< >>osp<< >>pap<< >>pcd<< >>pln<< >>pms<< >>pob<< >>por<< >>pov<< >>pre<< >>pro<< >>qbb<< >>qhr<< >>rcf<< >>rgn<< >>roh<< >>ron<< >>ruo<< >>rup<< >>ruq<< >>scf<< >>scn<< >>sdc<< >>sdn<< >>spa<< >>spq<< >>spx<< >>src<< >>srd<< >>sro<< >>tmg<< >>tvy<< >>vec<< >>vkp<< >>wln<< >>xfa<< >>xum<< - **Original Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fas-itc/opusTCv20210807_transformer-big_2022-07-23.zip) - **Resources for more information:** - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) - More information about released models for this language pair: [OPUS-MT fas-itc README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fas-itc/README.md) - [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/ This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>fra<<` ## Uses This model can be used for translation and text-to-text generation. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). ## How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>lad<< اسلام زیباست.", ">>spa<< ورود به کتابخانه رایگان است." ] model_name = "pytorch-models/opus-mt-tc-big-fa-itc" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # O Islam é lindo. # La entrada a la biblioteca es gratuita. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-fa-itc") print(pipe(">>lad<< اسلام زیباست.")) # expected output: O Islam é lindo. ``` ## Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fas-itc/opusTCv20210807_transformer-big_2022-07-23.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-07-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fas-itc/opusTCv20210807_transformer-big_2022-07-23.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-07-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fas-itc/opusTCv20210807_transformer-big_2022-07-23.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | fas-fra | tatoeba-test-v2021-08-07 | 0.57949 | 37.5 | 376 | 3377 | | fas-fra | flores101-devtest | 0.55883 | 28.9 | 1012 | 28343 | | fas-ita | flores101-devtest | 0.49512 | 19.7 | 1012 | 27306 | | fas-por | flores101-devtest | 0.54829 | 27.6 | 1012 | 26519 | | fas-ron | flores101-devtest | 0.48821 | 19.7 | 1012 | 26799 | | fas-spa | flores101-devtest | 0.47722 | 19.4 | 1012 | 29199 | ## Citation Information * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 8b9f0b0 * port time: Sat Aug 13 00:08:53 EEST 2022 * port machine: LM0-400-22516.local

a2c948bbe7481ad1742ec5e9b3aba7fa

DmitryPogrebnoy/MedDistilBertBaseRuCased

DmitryPogrebnoy

distilbert

transformers

fill-mask

apache-2.0

['ru']

[]

# Model DmitryPogrebnoy/MedDistilBertBaseRuCased # Model Description This model is fine-tuned version of [DmitryPogrebnoy/distilbert-base-russian-cased](https://huggingface.co/DmitryPogrebnoy/distilbert-base-russian-cased). The code for the fine-tuned process can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spellchecker/ml_ranging/models/med_distilbert_base_russian_cased/fine_tune_distilbert_base_russian_cased.py). The model is fine-tuned on a specially collected dataset of over 30,000 medical anamneses in Russian. The collected dataset can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/data/anamnesis/processed/all_anamnesis.csv). This model was created as part of a master's project to develop a method for correcting typos in medical histories using BERT models as a ranking of candidates. The project is open source and can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker). # How to Get Started With the Model You can use the model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> pipeline = pipeline('fill-mask', model='DmitryPogrebnoy/MedDistilBertBaseRuCased') >>> pipeline("У пациента [MASK] боль в грудине.") [{'score': 0.1733243614435196, 'token': 6880, 'token_str': 'имеется', 'sequence': 'У пациента имеется боль в грудине.'}, {'score': 0.08818087726831436, 'token': 1433, 'token_str': 'есть', 'sequence': 'У пациента есть боль в грудине.'}, {'score': 0.03620537742972374, 'token': 3793, 'token_str': 'особенно', 'sequence': 'У пациента особенно боль в грудине.'}, {'score': 0.03438418731093407, 'token': 5168, 'token_str': 'бол', 'sequence': 'У пациента бол боль в грудине.'}, {'score': 0.032936397939920425, 'token': 6281, 'token_str': 'протекает', 'sequence': 'У пациента протекает боль в грудине.'}] ``` Or you can load the model and tokenizer and do what you need to do: ```python >>> from transformers import AutoTokenizer, AutoModelForMaskedLM >>> tokenizer = AutoTokenizer.from_pretrained("DmitryPogrebnoy/MedDistilBertBaseRuCased") >>> model = AutoModelForMaskedLM.from_pretrained("DmitryPogrebnoy/MedDistilBertBaseRuCased") ```

fd7213be524b0f36697de322b67a056b

amartyobanerjee/bert-finetuned-ner

amartyobanerjee

bert

transformers

token-classification

apache-2.0

['conll2003']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0622 - Precision: 0.9314 - Recall: 0.9507 - F1: 0.9410 - Accuracy: 0.9863 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0821 | 1.0 | 1756 | 0.0639 | 0.9108 | 0.9371 | 0.9238 | 0.9834 | | 0.0366 | 2.0 | 3512 | 0.0585 | 0.9310 | 0.9497 | 0.9403 | 0.9857 | | 0.019 | 3.0 | 5268 | 0.0622 | 0.9314 | 0.9507 | 0.9410 | 0.9863 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

7db5987e855774fab53bf71050e73fcd

Helsinki-NLP/opus-mt-pt-tl

Helsinki-NLP

marian

transformers

translation

apache-2.0

['pt', 'tl']

['translation']

### por-tgl * source group: Portuguese * target group: Tagalog * OPUS readme: [por-tgl](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/por-tgl/README.md) * model: transformer-align * source language(s): por * target language(s): tgl_Latn * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/por-tgl/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/por-tgl/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/por-tgl/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.por.tgl | 28.4 | 0.565 | ### System Info: - hf_name: por-tgl - source_languages: por - target_languages: tgl - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/por-tgl/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['pt', 'tl'] - src_constituents: {'por'} - tgt_constituents: {'tgl_Latn'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/por-tgl/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/por-tgl/opus-2020-06-17.test.txt - src_alpha3: por - tgt_alpha3: tgl - short_pair: pt-tl - chrF2_score: 0.565 - bleu: 28.4 - brevity_penalty: 1.0 - ref_len: 13620.0 - src_name: Portuguese - tgt_name: Tagalog - train_date: 2020-06-17 - src_alpha2: pt - tgt_alpha2: tl - prefer_old: False - long_pair: por-tgl - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

be1b5b77920f27a3ba1d656692459b2a

jhaochenz/finetuned_distilgpt2_sst2_negation0.0001_pretrainedTrue_epochs1

jhaochenz

gpt2

transformers

text-generation

apache-2.0

['sst2']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_distilgpt2_sst2_negation0.0001_pretrainedTrue_epochs1 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.2798 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7111 | 1.0 | 1322 | 3.2798 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.7.0 - Datasets 2.8.0 - Tokenizers 0.13.2

fb01b1fc9a9f222fba58c58144df7f0f

farleyknight-org-username/vit-base-mnist

farleyknight-org-username

vit

transformers

image-classification

apache-2.0

['mnist']

['image-classification', 'vision', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-mnist This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the mnist dataset. It achieves the following results on the evaluation set: - Loss: 0.0236 - Accuracy: 0.9949 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3717 | 1.0 | 6375 | 0.0522 | 0.9893 | | 0.3453 | 2.0 | 12750 | 0.0370 | 0.9906 | | 0.3736 | 3.0 | 19125 | 0.0308 | 0.9916 | | 0.3224 | 4.0 | 25500 | 0.0269 | 0.9939 | | 0.2846 | 5.0 | 31875 | 0.0236 | 0.9949 | ### Framework versions - Transformers 4.22.0.dev0 - Pytorch 1.11.0a0+17540c5 - Datasets 2.4.0 - Tokenizers 0.12.1

7cb20593448115bc3bb6d2ac8d823bd7

vesteinn/fasttext_is_rmh

vesteinn

agpl-3.0

['is']

[]

# FastText model trained on Icelandic This model is trained on the lemmas of the Icelandic Gigaword Corpus version 20.05. It is trained using the gensim package, version 4.1.0. and parameters were set to default (100 dimensions, windows size 5) This model can not be loaded directly since it uses gensim, clone the repository and run the following to use it. ```python import gensim model = gensim.models.FastText.load("./rmh.w2v.model") ``` ## Example output ```bash In [1]: model.wv.most_similar("england") Out[1]: [('englands', 0.8778558969497681), ('southland', 0.8573296070098877), ('skotland', 0.846065878868103), ('englaland', 0.8320872187614441), ('hoogland', 0.8299505114555359), ('hoagland', 0.8277317881584167), ('totland', 0.8265103697776794), ('lackland', 0.8234561681747437), ('skarpengland', 0.8227219581604004), ('langland', 0.8222305774688721)] In [2]: model.wv.most_similar("kanína") Out[2]: [('loðkanína', 0.9271067976951599), ('dvergkanína', 0.9106121063232422), ('angórakanína', 0.895512044429779), ('angórukanína', 0.8741581439971924), ('feldkanína', 0.8696010708808899), ('kanínubangsi', 0.8562541604042053), ('holdakanína', 0.8543838858604431), ('villikanína', 0.8525990843772888), ('silkikanína', 0.8515204191207886), ('kaníni', 0.8445548415184021)] ```

67b024dad59e6b1b494414fcda681f02

JeremiahZ/reproduce-unsup-roberta-base-avg

JeremiahZ

roberta

transformers

mit

['en']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # reproduce-unsup-roberta-base-avg This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 512 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

c1e535b2239bf90bf99eaabce8291092

abyaugustinek/distilbert-base-uncased-finetuned

abyaugustinek

distilbert

transformers

token-classification

apache-2.0

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # abyaugustinek/distilbert-base-uncased-finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.3693 - Validation Loss: 1.2106 - Train Precision: 0.0 - Train Recall: 0.0 - Train F1: 0.0 - Train Accuracy: 0.6565 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 30, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 2.0691 | 1.5942 | 0.0 | 0.0 | 0.0 | 0.6565 | 0 | | 1.4705 | 1.2376 | 0.0 | 0.0 | 0.0 | 0.6565 | 1 | | 1.3693 | 1.2106 | 0.0 | 0.0 | 0.0 | 0.6565 | 2 | ### Framework versions - Transformers 4.21.0 - TensorFlow 2.7.0 - Datasets 2.3.2 - Tokenizers 0.12.1

0a31b3f0d07889cc7ab2c9801cd855a7

celine98/canine-s-finetuned-sst2

celine98

canine

transformers

text-classification

apache-2.0

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # canine-s-finetuned-sst2 This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5259 - Accuracy: 0.8578 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3524 | 1.0 | 4210 | 0.4762 | 0.8257 | | 0.2398 | 2.0 | 8420 | 0.4169 | 0.8567 | | 0.1797 | 3.0 | 12630 | 0.5259 | 0.8578 | | 0.152 | 4.0 | 16840 | 0.5996 | 0.8532 | | 0.1026 | 5.0 | 21050 | 0.6676 | 0.8578 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

a0fcef5435cbc9ee297b80bd3f16e03f

Helsinki-NLP/opus-mt-fi-en

Helsinki-NLP

marian

transformers

translation

apache-2.0

['fi', 'en']

['translation']

### fin-eng * source group: Finnish * target group: English * OPUS readme: [fin-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-eng/README.md) * model: transformer-align * source language(s): fin * target language(s): eng * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-08-05.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.zip) * test set translations: [opus-2020-08-05.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.test.txt) * test set scores: [opus-2020-08-05.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2015-enfi-fineng.fin.eng | 25.3 | 0.536 | | newstest2015-enfi-fineng.fin.eng | 26.9 | 0.547 | | newstest2016-enfi-fineng.fin.eng | 29.0 | 0.571 | | newstest2017-enfi-fineng.fin.eng | 32.3 | 0.594 | | newstest2018-enfi-fineng.fin.eng | 23.8 | 0.517 | | newstest2019-fien-fineng.fin.eng | 29.0 | 0.565 | | newstestB2016-enfi-fineng.fin.eng | 24.5 | 0.527 | | newstestB2017-enfi-fineng.fin.eng | 27.4 | 0.557 | | newstestB2017-fien-fineng.fin.eng | 27.4 | 0.557 | | Tatoeba-test.fin.eng | 53.4 | 0.697 | ### System Info: - hf_name: fin-eng - source_languages: fin - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['fi', 'en'] - src_constituents: {'fin'} - tgt_constituents: {'eng'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.test.txt - src_alpha3: fin - tgt_alpha3: eng - short_pair: fi-en - chrF2_score: 0.6970000000000001 - bleu: 53.4 - brevity_penalty: 0.99 - ref_len: 74651.0 - src_name: Finnish - tgt_name: English - train_date: 2020-08-05 - src_alpha2: fi - tgt_alpha2: en - prefer_old: False - long_pair: fin-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

fd1c9c87004561b6ee405089b3fe0ce1

groar/distilgpt2-finetuned-wikitext2

groar

gpt2

transformers

text-generation

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6895 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7852 | 1.0 | 2334 | 3.6895 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

c575780dd6439fc19b8441af8fb014c8

mshoaibsarwar/finetuning-sentiment-model-samples

mshoaibsarwar

distilbert

transformers

text-classification

apache-2.0

['imdb']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

363ee81c0eabcf08c48a15de7bbed8be

jonatasgrosman/exp_w2v2r_fr_xls-r_accent_france-0_belgium-10_s198

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['fr']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'fr']

# exp_w2v2r_fr_xls-r_accent_france-0_belgium-10_s198 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

487a51881b3bef1a55fe6c7e2c8ecb84

PaddlePaddle/ernie-m-large

PaddlePaddle

ernie_m

paddlenlp

apache-2.0

['fr', 'es', 'en', 'de', 'sw', 'ru', 'zh', 'el', 'bg', 'ar', 'vi', 'th', 'hi', 'ur']

['xnli', 'mlqa', 'paws-x']

[]

[](https://github.com/PaddlePaddle/PaddleNLP) # PaddlePaddle/ernie-m-base ## Ernie-M ERNIE-M, proposed by Baidu, is a new training method that encourages the model to align the representation of multiple languages with monolingual corpora, to overcome the constraint that the parallel corpus size places on the model performance. The insight is to integrate back-translation into the pre-training process by generating pseudo-parallel sentence pairs on a monolingual corpus to enable the learning of semantic alignments between different languages, thereby enhancing the semantic modeling of cross-lingual models. Experimental results show that ERNIE-M outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-lingual downstream tasks. We proposed two novel methods to align the representation of multiple languages: Cross-Attention Masked Language Modeling(CAMLM): In CAMLM, we learn the multilingual semantic representation by restoring the MASK tokens in the input sentences. Back-Translation masked language modeling(BTMLM): We use BTMLM to train our model to generate pseudo-parallel sentences from the monolingual sentences. The generated pairs are then used as the input of the model to further align the cross-lingual semantics, thus enhancing the multilingual representation.  ## Benchmark ### XNLI XNLI is a subset of MNLI and has been translated into 14 different kinds of languages including some low-resource languages. The goal of the task is to predict testual entailment (whether sentence A implies / contradicts / neither sentence B). | Model | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur | Avg | | ---------------------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | | Cross-lingual Transfer | | | | | | | | | | | | | | | | | | XLM | 85.0 | 78.7 | 78.9 | 77.8 | 76.6 | 77.4 | 75.3 | 72.5 | 73.1 | 76.1 | 73.2 | 76.5 | 69.6 | 68.4 | 67.3 | 75.1 | | Unicoder | 85.1 | 79.0 | 79.4 | 77.8 | 77.2 | 77.2 | 76.3 | 72.8 | 73.5 | 76.4 | 73.6 | 76.2 | 69.4 | 69.7 | 66.7 | 75.4 | | XLM-R | 85.8 | 79.7 | 80.7 | 78.7 | 77.5 | 79.6 | 78.1 | 74.2 | 73.8 | 76.5 | 74.6 | 76.7 | 72.4 | 66.5 | 68.3 | 76.2 | | INFOXLM | **86.4** | **80.6** | 80.8 | 78.9 | 77.8 | 78.9 | 77.6 | 75.6 | 74.0 | 77.0 | 73.7 | 76.7 | 72.0 | 66.4 | 67.1 | 76.2 | | **ERNIE-M** | 85.5 | 80.1 | **81.2** | **79.2** | **79.1** | **80.4** | **78.1** | **76.8** | **76.3** | **78.3** | **75.8** | **77.4** | **72.9** | **69.5** | **68.8** | **77.3** | | XLM-R Large | 89.1 | 84.1 | 85.1 | 83.9 | 82.9 | 84.0 | 81.2 | 79.6 | 79.8 | 80.8 | 78.1 | 80.2 | 76.9 | 73.9 | 73.8 | 80.9 | | INFOXLM Large | **89.7** | 84.5 | 85.5 | 84.1 | 83.4 | 84.2 | 81.3 | 80.9 | 80.4 | 80.8 | 78.9 | 80.9 | 77.9 | 74.8 | 73.7 | 81.4 | | VECO Large | 88.2 | 79.2 | 83.1 | 82.9 | 81.2 | 84.2 | 82.8 | 76.2 | 80.3 | 74.3 | 77.0 | 78.4 | 71.3 | **80.4** | **79.1** | 79.9 | | **ERNIR-M Large** | 89.3 | **85.1** | **85.7** | **84.4** | **83.7** | **84.5** | 82.0 | **81.2** | **81.2** | **81.9** | **79.2** | **81.0** | **78.6** | 76.2 | 75.4 | **82.0** | | Translate-Train-All | | | | | | | | | | | | | | | | | | XLM | 85.0 | 80.8 | 81.3 | 80.3 | 79.1 | 80.9 | 78.3 | 75.6 | 77.6 | 78.5 | 76.0 | 79.5 | 72.9 | 72.8 | 68.5 | 77.8 | | Unicoder | 85.6 | 81.1 | 82.3 | 80.9 | 79.5 | 81.4 | 79.7 | 76.8 | 78.2 | 77.9 | 77.1 | 80.5 | 73.4 | 73.8 | 69.6 | 78.5 | | XLM-R | 85.4 | 81.4 | 82.2 | 80.3 | 80.4 | 81.3 | 79.7 | 78.6 | 77.3 | 79.7 | 77.9 | 80.2 | 76.1 | 73.1 | 73.0 | 79.1 | | INFOXLM | 86.1 | 82.0 | 82.8 | 81.8 | 80.9 | 82.0 | 80.2 | 79.0 | 78.8 | 80.5 | 78.3 | 80.5 | 77.4 | 73.0 | 71.6 | 79.7 | | **ERNIE-M** | **86.2** | **82.5** | **83.8** | **82.6** | **82.4** | **83.4** | **80.2** | **80.6** | **80.5** | **81.1** | **79.2** | **80.5** | **77.7** | **75.0** | **73.3** | **80.6** | | XLM-R Large | 89.1 | 85.1 | 86.6 | 85.7 | 85.3 | 85.9 | 83.5 | 83.2 | 83.1 | 83.7 | 81.5 | **83.7** | **81.6** | 78.0 | 78.1 | 83.6 | | VECO Large | 88.9 | 82.4 | 86.0 | 84.7 | 85.3 | 86.2 | **85.8** | 80.1 | 83.0 | 77.2 | 80.9 | 82.8 | 75.3 | **83.1** | **83.0** | 83.0 | | **ERNIE-M Large** | **89.5** | **86.5** | **86.9** | **86.1** | **86.0** | **86.8** | 84.1 | **83.8** | **84.1** | **84.5** | **82.1** | 83.5 | 81.1 | 79.4 | 77.9 | **84.2** | ### Cross-lingual Named Entity Recognition * datasets：CoNLI | Model | en | nl | es | de | Avg | | ------------------------------ | --------- | --------- | --------- | --------- | --------- | | *Fine-tune on English dataset* | | | | | | | mBERT | 91.97 | 77.57 | 74.96 | 69.56 | 78.52 | | XLM-R | 92.25 | **78.08** | 76.53 | **69.60** | 79.11 | | **ERNIE-M** | **92.78** | 78.01 | **79.37** | 68.08 | **79.56** | | XLM-R LARGE | 92.92 | 80.80 | 78.64 | 71.40 | 80.94 | | **ERNIE-M LARGE** | **93.28** | **81.45** | **78.83** | **72.99** | **81.64** | | *Fine-tune on all dataset* | | | | | | | XLM-R | 91.08 | 89.09 | 87.28 | 83.17 | 87.66 | | **ERNIE-M** | **93.04** | **91.73** | **88.33** | **84.20** | **89.32** | | XLM-R LARGE | 92.00 | 91.60 | **89.52** | 84.60 | 89.43 | | **ERNIE-M LARGE** | **94.01** | **93.81** | 89.23 | **86.20** | **90.81** | ### Cross-lingual Question Answering * datasets：MLQA | Model | en | es | de | ar | hi | vi | zh | Avg | | ----------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- | | mBERT | 77.7 / 65.2 | 64.3 / 46.6 | 57.9 / 44.3 | 45.7 / 29.8 | 43.8 / 29.7 | 57.1 / 38.6 | 57.5 / 37.3 | 57.7 / 41.6 | | XLM | 74.9 / 62.4 | 68.0 / 49.8 | 62.2 / 47.6 | 54.8 / 36.3 | 48.8 / 27.3 | 61.4 / 41.8 | 61.1 / 39.6 | 61.6 / 43.5 | | XLM-R | 77.1 / 64.6 | 67.4 / 49.6 | 60.9 / 46.7 | 54.9 / 36.6 | 59.4 / 42.9 | 64.5 / 44.7 | 61.8 / 39.3 | 63.7 / 46.3 | | INFOXLM | 81.3 / 68.2 | 69.9 / 51.9 | 64.2 / 49.6 | 60.1 / 40.9 | 65.0 / 47.5 | 70.0 / 48.6 | 64.7 / **41.2** | 67.9 / 49.7 | | **ERNIE-M** | **81.6 / 68.5** | **70.9 / 52.6** | **65.8 / 50.7** | **61.8 / 41.9** | **65.4 / 47.5** | **70.0 / 49.2** | **65.6** / 41.0 | **68.7 / 50.2** | | XLM-R LARGE | 80.6 / 67.8 | 74.1 / 56.0 | 68.5 / 53.6 | 63.1 / 43.5 | 62.9 / 51.6 | 71.3 / 50.9 | 68.0 / 45.4 | 70.7 / 52.7 | | INFOXLM LARGE | **84.5 / 71.6** | **75.1 / 57.3** | **71.2 / 56.2** | **67.6 / 47.6** | 72.5 / 54.2 | **75.2 / 54.1** | 69.2 / 45.4 | 73.6 / 55.2 | | **ERNIE-M LARGE** | 84.4 / 71.5 | 74.8 / 56.6 | 70.8 / 55.9 | 67.4 / 47.2 | **72.6 / 54.7** | 75.0 / 53.7 | **71.1 / 47.5** | **73.7 / 55.3** | ### Cross-lingual Paraphrase Identification * datasets：PAWS-X | Model | en | de | es | fr | ja | ko | zh | Avg | | ---------------------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | | Cross-lingual Transfer | | | | | | | | | | mBERT | 94.0 | 85.7 | 87.4 | 87.0 | 73.0 | 69.6 | 77.0 | 81.9 | | XLM | 94.0 | 85.9 | 88.3 | 87.4 | 69.3 | 64.8 | 76.5 | 80.9 | | MMTE | 93.1 | 85.1 | 87.2 | 86.9 | 72.0 | 69.2 | 75.9 | 81.3 | | XLM-R LARGE | 94.7 | 89.7 | 90.1 | 90.4 | 78.7 | 79.0 | 82.3 | 86.4 | | VECO LARGE | **96.2** | 91.3 | 91.4 | 92.0 | 81.8 | 82.9 | 85.1 | 88.7 | | **ERNIE-M LARGE** | 96.0 | **91.9** | **91.4** | **92.2** | **83.9** | **84.5** | **86.9** | **89.5** | | Translate-Train-All | | | | | | | | | | VECO LARGE | 96.4 | 93.0 | 93.0 | 93.5 | 87.2 | 86.8 | 87.9 | 91.1 | | **ERNIE-M LARGE** | **96.5** | **93.5** | **93.3** | **93.8** | **87.9** | **88.4** | **89.2** | **91.8** | ### Cross-lingual Sentence Retrieval * dataset：Tatoeba | Model | Avg | | --------------------------------------- | -------- | | XLM-R LARGE | 75.2 | | VECO LARGE | 86.9 | | **ERNIE-M LARGE** | **87.9** | | **ERNIE-M LARGE（ after fine-tuning）** | **93.3** | ## Citation Info ```text @article{Ouyang2021ERNIEMEM, title={ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora}, author={Xuan Ouyang and Shuohuan Wang and Chao Pang and Yu Sun and Hao Tian and Hua Wu and Haifeng Wang}, journal={ArXiv}, year={2021}, volume={abs/2012.15674} } ```

57650c4833830b0f2c96e68358655206

responsibility-framing/predict-perception-xlmr-focus-victim

responsibility-framing

xlm-roberta

transformers

text-classification

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # predict-perception-xlmr-focus-victim This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2546 - Rmse: 0.6301 - Rmse Focus::a Sulla vittima: 0.6301 - Mae: 0.5441 - Mae Focus::a Sulla vittima: 0.5441 - R2: 0.7205 - R2 Focus::a Sulla vittima: 0.7205 - Cos: 0.8261 - Pair: 0.0 - Rank: 0.5 - Neighbors: 0.7802 - Rsa: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 20 - eval_batch_size: 8 - seed: 1996 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Sulla vittima | Mae | Mae Focus::a Sulla vittima | R2 | R2 Focus::a Sulla vittima | Cos | Pair | Rank | Neighbors | Rsa | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:| | 1.0607 | 1.0 | 15 | 0.9261 | 1.2017 | 1.2017 | 0.9557 | 0.9557 | -0.0166 | -0.0166 | 0.4783 | 0.0 | 0.5 | 0.6332 | nan | | 1.0107 | 2.0 | 30 | 0.9481 | 1.2159 | 1.2159 | 0.9861 | 0.9861 | -0.0408 | -0.0408 | 0.4783 | 0.0 | 0.5 | 0.6332 | nan | | 0.9921 | 3.0 | 45 | 0.9068 | 1.1892 | 1.1892 | 0.9548 | 0.9548 | 0.0045 | 0.0045 | 0.4783 | 0.0 | 0.5 | 0.6332 | nan | | 0.7769 | 4.0 | 60 | 0.5014 | 0.8842 | 0.8842 | 0.7121 | 0.7121 | 0.4496 | 0.4496 | 0.7391 | 0.0 | 0.5 | 0.6232 | nan | | 0.5763 | 5.0 | 75 | 0.4019 | 0.7917 | 0.7917 | 0.6737 | 0.6737 | 0.5588 | 0.5588 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan | | 0.4378 | 6.0 | 90 | 0.3594 | 0.7486 | 0.7486 | 0.5957 | 0.5957 | 0.6055 | 0.6055 | 0.7391 | 0.0 | 0.5 | 0.4442 | nan | | 0.3595 | 7.0 | 105 | 0.3452 | 0.7337 | 0.7337 | 0.6333 | 0.6333 | 0.6210 | 0.6210 | 0.5652 | 0.0 | 0.5 | 0.2649 | nan | | 0.3192 | 8.0 | 120 | 0.3275 | 0.7147 | 0.7147 | 0.6205 | 0.6205 | 0.6405 | 0.6405 | 0.7391 | 0.0 | 0.5 | 0.6561 | nan | | 0.2482 | 9.0 | 135 | 0.2978 | 0.6815 | 0.6815 | 0.5754 | 0.5754 | 0.6731 | 0.6731 | 0.7391 | 0.0 | 0.5 | 0.6715 | nan | | 0.2416 | 10.0 | 150 | 0.3018 | 0.6860 | 0.6860 | 0.5954 | 0.5954 | 0.6687 | 0.6687 | 0.5652 | 0.0 | 0.5 | 0.2553 | nan | | 0.2292 | 11.0 | 165 | 0.2764 | 0.6565 | 0.6565 | 0.5522 | 0.5522 | 0.6966 | 0.6966 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan | | 0.1752 | 12.0 | 180 | 0.3070 | 0.6920 | 0.6920 | 0.5680 | 0.5680 | 0.6629 | 0.6629 | 0.7391 | 0.0 | 0.5 | 0.6715 | nan | | 0.1956 | 13.0 | 195 | 0.2923 | 0.6752 | 0.6752 | 0.5499 | 0.5499 | 0.6791 | 0.6791 | 0.8261 | 0.0 | 0.5 | 0.7843 | nan | | 0.1424 | 14.0 | 210 | 0.3163 | 0.7023 | 0.7023 | 0.6060 | 0.6060 | 0.6528 | 0.6528 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan | | 0.152 | 15.0 | 225 | 0.2436 | 0.6164 | 0.6164 | 0.5127 | 0.5127 | 0.7326 | 0.7326 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan | | 0.1277 | 16.0 | 240 | 0.2471 | 0.6208 | 0.6208 | 0.5367 | 0.5367 | 0.7287 | 0.7287 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.1269 | 17.0 | 255 | 0.2573 | 0.6334 | 0.6334 | 0.5329 | 0.5329 | 0.7175 | 0.7175 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.1058 | 18.0 | 270 | 0.2538 | 0.6291 | 0.6291 | 0.5530 | 0.5530 | 0.7214 | 0.7214 | 0.7391 | 0.0 | 0.5 | 0.2347 | nan | | 0.107 | 19.0 | 285 | 0.2568 | 0.6328 | 0.6328 | 0.5464 | 0.5464 | 0.7181 | 0.7181 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.1185 | 20.0 | 300 | 0.2452 | 0.6183 | 0.6183 | 0.5317 | 0.5317 | 0.7309 | 0.7309 | 0.7391 | 0.0 | 0.5 | 0.2347 | nan | | 0.1029 | 21.0 | 315 | 0.2419 | 0.6142 | 0.6142 | 0.5415 | 0.5415 | 0.7344 | 0.7344 | 0.7391 | 0.0 | 0.5 | 0.2347 | nan | | 0.0908 | 22.0 | 330 | 0.2462 | 0.6196 | 0.6196 | 0.5261 | 0.5261 | 0.7297 | 0.7297 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.0901 | 23.0 | 345 | 0.2528 | 0.6279 | 0.6279 | 0.5330 | 0.5330 | 0.7225 | 0.7225 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.0979 | 24.0 | 360 | 0.2800 | 0.6607 | 0.6607 | 0.5682 | 0.5682 | 0.6927 | 0.6927 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan | | 0.0992 | 25.0 | 375 | 0.2502 | 0.6246 | 0.6246 | 0.5517 | 0.5517 | 0.7254 | 0.7254 | 0.6522 | 0.0 | 0.5 | 0.2372 | nan | | 0.0846 | 26.0 | 390 | 0.2570 | 0.6331 | 0.6331 | 0.5524 | 0.5524 | 0.7178 | 0.7178 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.0717 | 27.0 | 405 | 0.2562 | 0.6321 | 0.6321 | 0.5456 | 0.5456 | 0.7187 | 0.7187 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.0739 | 28.0 | 420 | 0.2570 | 0.6330 | 0.6330 | 0.5471 | 0.5471 | 0.7179 | 0.7179 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.0828 | 29.0 | 435 | 0.2553 | 0.6309 | 0.6309 | 0.5446 | 0.5446 | 0.7198 | 0.7198 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.086 | 30.0 | 450 | 0.2546 | 0.6301 | 0.6301 | 0.5441 | 0.5441 | 0.7205 | 0.7205 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

3bf33957ecf48466fb7cb71a72141068

nielsr/nt5-small-rc1

nielsr

t5

transformers

text2text-generation

apache-2.0

['drop']

[]

# NT5, a T5 model trained to perform numerical reasoning T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on [DROP](https://allennlp.org/drop.html). It was introduced in the paper [NT5?! Training T5 to Perform Numerical Reasoning](https://arxiv.org/abs/2104.07307) by Yang et al. and first released in [this repository](https://github.com/lesterpjy/numeric-t5). As the original implementation was in Tensorflow 2, I've converted the weigths to PyTorch. This model corresponds to RC Experiment 1 (see the paper), their best performing model. Disclaimer: The team releasing NT5 did not write a model card for this model so this model card has been written by me. ## Model description The NT5 model is a T5 model, in other words, an encoder-decoder Transformer. In order to encourage numerical reasoning, the model was further pre-trained on three datasets designed to strengthen skills necessary for numerical reasoning over text (NRoT) and general reading comprehension before being fine-tuned on the Discrete Reasoning over Text (DROP) dataset. ## Intended uses & limitations You can use the model for numerical reasoning over text. ### How to use Here is how to use this model: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration context = """Saint Jean de Brébeuf was a French Jesuit missionary who travelled to New France in 1625. There he worked primarily with the Huron for the rest of his life, except for a few years in France from 1629 to 1633. He learned their language and culture, writing extensively about each to aid other missionaries. In 1649, Br´ebeuf and another missionary were captured when an Iroquois raid took over a Huron village . Together with Huron captives, the missionaries were ritually tortured and killed on March 16, 1649. Br´ebeuf was beatified in 1925 and among eight Jesuit missionaries canonized as saints in the Roman Catholic Church in 1930.""" question = "How many years did Saint Jean de Brébeuf stay in New France before he went back to France for a few years?" tokenizer = T5Tokenizer.from_pretrained("nielsr/nt5-small-rc1") model = T5ForConditionalGeneration.from_pretrained("nielsr/nt5-small-rc1") # encode context & question input_text = f"answer_me: {question} context: {context}" encoded_query = tokenizer( input_text, return_tensors='pt', padding='max_length', truncation=True, max_length=512) # generate answer generated_answer = model.generate(input_ids=encoded_query["input_ids"], attention_mask=encoded_query["attention_mask"], max_length=54) decoded_answer = tokenizer.decode(generated_answer.numpy()[0]) print("T5 Answer: ", decoded_answer) T5 Answer: 4 ``` ## Evaluation results This model achieves an F1 score of 0.7031 and exact match of 0.6687 on the development set of DROP. ### BibTeX entry and citation info ```bibtex @misc{yang2021nt5, title={NT5?! Training T5 to Perform Numerical Reasoning}, author={Peng-Jian Yang and Ying Ting Chen and Yuechan Chen and Daniel Cer}, year={2021}, eprint={2104.07307}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @article{DBLP:journals/corr/abs-1903-00161, author = {Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner}, title = {{DROP:} {A} Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs}, journal = {CoRR}, volume = {abs/1903.00161}, year = {2019}, url = {http://arxiv.org/abs/1903.00161}, archivePrefix = {arXiv}, eprint = {1903.00161}, timestamp = {Wed, 03 Jul 2019 07:17:04 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1903-00161.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } a service of Schloss Dagstuhl - Leibniz Center for Informatics\\\\thomebrowsesearchabout ```

b540745020a818731eda3ce899c58b69

csikasote/xlsr-53-bemba-10hrs

csikasote

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlsr-53-bemba-10hrs This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3190 - Wer: 0.4032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3207 | 1.07 | 400 | 0.3720 | 0.5923 | | 0.5688 | 2.14 | 800 | 0.3073 | 0.5002 | | 0.3927 | 3.22 | 1200 | 0.2678 | 0.4521 | | 0.316 | 4.29 | 1600 | 0.2703 | 0.4261 | | 0.2531 | 5.36 | 2000 | 0.2663 | 0.4198 | | 0.2051 | 6.43 | 2400 | 0.2614 | 0.4037 | | 0.1584 | 7.51 | 2800 | 0.2853 | 0.4046 | | 0.1343 | 8.58 | 3200 | 0.3072 | 0.4121 | | 0.1031 | 9.65 | 3600 | 0.3190 | 0.4032 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1

7173341e150869e8c8d1b491e411b7ca

jonatasgrosman/exp_w2v2r_de_vp-100k_age_teens-2_sixties-8_s877

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['de']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'de']

# exp_w2v2r_de_vp-100k_age_teens-2_sixties-8_s877 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

084836cb11805feb241ed2f905e45981

Go2Heart/BERT_Mod_2

Go2Heart

distilbert

transformers

text-classification

apache-2.0

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERT_Mod_2 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5659 - eval_accuracy: 0.9037 - eval_runtime: 0.3838 - eval_samples_per_second: 2271.724 - eval_steps_per_second: 143.285 - epoch: 0.01 - step: 49 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0 - Datasets 2.4.0 - Tokenizers 0.12.1

af593e5ce812e1bd58d08b527cb8f9cf

jkhan447/sarcasm-detection-Bert-base-uncased-POS

jkhan447

bert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sarcasm-detection-Bert-base-uncased-POS This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1904 - Accuracy: 0.591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.3.0 - Tokenizers 0.12.1

a8d79419c610a983fc2f35e52013ffdd

Helsinki-NLP/opus-mt-sv-xh

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-sv-xh * source languages: sv * target languages: xh * OPUS readme: [sv-xh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-xh/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-xh/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-xh/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-xh/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.xh | 26.7 | 0.561 |

71bbfcc71ccc84ea466d09e8cc1d3cbf

Gladiator/microsoft-deberta-v3-large_ner_conll2003

Gladiator

deberta-v2

transformers

token-classification

mit

['conll2003']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # microsoft-deberta-v3-large_ner_conll2003 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0293 - Precision: 0.9667 - Recall: 0.9724 - F1: 0.9695 - Accuracy: 0.9945 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0986 | 1.0 | 878 | 0.0323 | 0.9453 | 0.9596 | 0.9524 | 0.9921 | | 0.0212 | 2.0 | 1756 | 0.0270 | 0.9571 | 0.9675 | 0.9623 | 0.9932 | | 0.009 | 3.0 | 2634 | 0.0280 | 0.9638 | 0.9714 | 0.9676 | 0.9940 | | 0.0035 | 4.0 | 3512 | 0.0290 | 0.9657 | 0.9712 | 0.9685 | 0.9943 | | 0.0022 | 5.0 | 4390 | 0.0293 | 0.9667 | 0.9724 | 0.9695 | 0.9945 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

39612d0797d739fd477c87d38c79298e

alexcaillet/ddpm-butterflies-128

alexcaillet

diffusers

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

[]

<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/alexcaillet/ddpm-butterflies-128/tensorboard?#scalars)

ad7b91bc39cbcef26ac2ed52d9939f9f

AndrewR/distilgpt2-finetuned-imdb-lm

AndrewR

gpt2

transformers

text-generation

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-imdb-lm This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8512 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.9577 | 1.0 | 7315 | 3.8818 | | 3.8965 | 2.0 | 14630 | 3.8570 | | 3.8561 | 3.0 | 21945 | 3.8512 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

27288f1adcde48ce3d6ec7c82bdd7b75

jonatasgrosman/exp_w2v2t_sv-se_vp-it_s975

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['sv-SE']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'sv-SE']

# exp_w2v2t_sv-se_vp-it_s975 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

967e0f73b0f66078197154f0009b7790

IIIT-L/roberta-large-finetuned-code-mixed-DS

IIIT-L

roberta

transformers

text-classification

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-finetuned-code-mixed-DS This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1340 - Accuracy: 0.7203 - Precision: 0.6584 - Recall: 0.6548 - F1: 0.6558 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9729 | 1.0 | 248 | 0.7491 | 0.6922 | 0.6434 | 0.6625 | 0.6358 | | 0.7474 | 1.99 | 496 | 0.6947 | 0.7183 | 0.6712 | 0.6915 | 0.6760 | | 0.5938 | 2.99 | 744 | 0.7370 | 0.7123 | 0.6624 | 0.6839 | 0.6642 | | 0.4264 | 3.98 | 992 | 0.8820 | 0.7123 | 0.6540 | 0.6636 | 0.6492 | | 0.2806 | 4.98 | 1240 | 1.2022 | 0.7404 | 0.6807 | 0.6694 | 0.6742 | | 0.2239 | 5.98 | 1488 | 1.3933 | 0.7223 | 0.6593 | 0.6587 | 0.6568 | | 0.1585 | 6.97 | 1736 | 1.8543 | 0.7304 | 0.6730 | 0.6763 | 0.6737 | | 0.1302 | 7.97 | 1984 | 2.0783 | 0.7143 | 0.6495 | 0.6520 | 0.6504 | | 0.1008 | 8.96 | 2232 | 2.3523 | 0.7183 | 0.6588 | 0.6561 | 0.6552 | | 0.0793 | 9.96 | 2480 | 2.5260 | 0.7163 | 0.6516 | 0.6566 | 0.6538 | | 0.0498 | 10.96 | 2728 | 2.6074 | 0.7425 | 0.6902 | 0.6817 | 0.6830 | | 0.0484 | 11.95 | 2976 | 2.6758 | 0.7284 | 0.6687 | 0.6734 | 0.6709 | | 0.0409 | 12.95 | 3224 | 2.8658 | 0.7425 | 0.6817 | 0.6756 | 0.6781 | | 0.0239 | 13.94 | 3472 | 2.9484 | 0.7465 | 0.6980 | 0.6818 | 0.6870 | | 0.025 | 14.94 | 3720 | 3.0827 | 0.7304 | 0.6778 | 0.6577 | 0.6641 | | 0.0286 | 15.94 | 3968 | 3.0011 | 0.7183 | 0.6509 | 0.6475 | 0.6491 | | 0.0264 | 16.93 | 4216 | 3.1581 | 0.7264 | 0.6645 | 0.6563 | 0.6595 | | 0.009 | 17.93 | 4464 | 3.1200 | 0.7223 | 0.6589 | 0.6561 | 0.6569 | | 0.012 | 18.92 | 4712 | 3.1364 | 0.7203 | 0.6573 | 0.6503 | 0.6525 | | 0.017 | 19.92 | 4960 | 3.1340 | 0.7203 | 0.6584 | 0.6548 | 0.6558 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.10.1+cu111 - Datasets 2.3.2 - Tokenizers 0.12.1

e45f9cec2d0796c6c4d40061fdf8138a

Keneston/distilbert-base-uncased-finetuned-emotion

Keneston

distilbert

transformers

text-classification

apache-2.0

['emotion']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2214 - Accuracy: 0.9275 - F1: 0.9274 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8568 | 1.0 | 250 | 0.3328 | 0.9 | 0.8947 | | 0.2576 | 2.0 | 500 | 0.2214 | 0.9275 | 0.9274 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

e3cbfe91d1e60b669d5720bbdbed3324

Salesforce/codegen-6B-mono

Salesforce

codegen

transformers

text-generation

bsd-3-clause

[]

# CodeGen (CodeGen-Mono 6B) ## Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`). The checkpoint included in this repository is denoted as **CodeGen-Mono 6B** in the paper, where "Mono" means the model is initialized with *CodeGen-Multi 6B* and further pre-trained on a Python programming language dataset, and "6B" refers to the number of trainable parameters. ## Training data This checkpoint (CodeGen-Mono 6B) was firstly initialized with *CodeGen-Multi 6B*, and then pre-trained on BigPython dataset. The data consists of 71.7B tokens of Python programming language. See Section 2.1 of the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Training procedure CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism. See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Evaluation results We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Intended Use and Limitations As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well. ## How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-6B-mono") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-6B-mono") text = "def hello_world():" input_ids = tokenizer(text, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=128) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) ``` ## BibTeX entry and citation info ```bibtex @article{Nijkamp2022ACP, title={A Conversational Paradigm for Program Synthesis}, author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming}, journal={arXiv preprint}, year={2022} } ```

88ac0d512da89b9c091387665c74d7d5

dbmdz/bert-base-italian-cased

dbmdz

bert

transformers

fill-mask