Finetuned Helsinki_sg_inf_en model
Browse files- README.md +87 -195
- generation_config.json +16 -0
README.md
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library_name: transformers
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---
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###
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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---
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library_name: transformers
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license: apache-2.0
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base_model: Helsinki-NLP/opus-mt-sg-en
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tags:
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- generated_from_trainer
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metrics:
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- bleu
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model-index:
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- name: Helsinki_sg_inf_en
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Helsinki_sg_inf_en
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This model is a fine-tuned version of [Helsinki-NLP/opus-mt-sg-en](https://huggingface.co/Helsinki-NLP/opus-mt-sg-en) on the Luganda Informal Data dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2858
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- Bleu: 5.572
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- Gen Len: 9.2338
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 30
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
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| No log | 1.0 | 173 | 0.6201 | 0.0669 | 6.4272 |
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| No log | 2.0 | 346 | 0.5838 | 0.0805 | 5.9299 |
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| 0.6387 | 3.0 | 519 | 0.5608 | 0.0808 | 6.1499 |
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| 0.6387 | 4.0 | 692 | 0.5425 | 0.1017 | 8.1706 |
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| 0.6387 | 5.0 | 865 | 0.5253 | 0.1307 | 8.0857 |
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| 0.5575 | 6.0 | 1038 | 0.5073 | 0.1321 | 9.5706 |
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| 0.5575 | 7.0 | 1211 | 0.4894 | 0.1799 | 12.0178 |
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| 0.5575 | 8.0 | 1384 | 0.4735 | 0.4182 | 14.6461 |
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| 0.5174 | 9.0 | 1557 | 0.4570 | 0.6454 | 8.5702 |
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| 0.5174 | 10.0 | 1730 | 0.4399 | 0.7703 | 10.7481 |
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| 0.5174 | 11.0 | 1903 | 0.4245 | 1.0412 | 10.367 |
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| 0.4806 | 12.0 | 2076 | 0.4091 | 0.9907 | 11.4105 |
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| 0.4806 | 13.0 | 2249 | 0.3942 | 1.3776 | 10.6073 |
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| 0.4806 | 14.0 | 2422 | 0.3828 | 1.682 | 9.6396 |
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| 0.4459 | 15.0 | 2595 | 0.3686 | 1.9324 | 10.269 |
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| 0.4459 | 16.0 | 2768 | 0.3588 | 2.1982 | 10.6911 |
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| 0.4459 | 17.0 | 2941 | 0.3477 | 2.7115 | 10.718 |
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| 0.4168 | 18.0 | 3114 | 0.3381 | 3.0707 | 9.9858 |
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| 0.4168 | 19.0 | 3287 | 0.3296 | 3.327 | 9.5866 |
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| 0.4168 | 20.0 | 3460 | 0.3212 | 3.6662 | 9.3844 |
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| 0.3955 | 21.0 | 3633 | 0.3153 | 4.0484 | 9.2152 |
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| 0.3955 | 22.0 | 3806 | 0.3087 | 4.5079 | 9.3263 |
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| 0.3955 | 23.0 | 3979 | 0.3026 | 4.7791 | 9.8225 |
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| 0.3756 | 24.0 | 4152 | 0.2987 | 5.0946 | 9.6704 |
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| 0.3756 | 25.0 | 4325 | 0.2950 | 5.1408 | 9.2835 |
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| 0.3756 | 26.0 | 4498 | 0.2917 | 5.3052 | 9.277 |
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| 0.3633 | 27.0 | 4671 | 0.2891 | 5.4256 | 9.7289 |
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| 0.3633 | 28.0 | 4844 | 0.2873 | 5.5373 | 9.5742 |
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| 0.3563 | 29.0 | 5017 | 0.2861 | 5.5454 | 10.1187 |
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| 0.3563 | 30.0 | 5190 | 0.2858 | 5.572 | 9.2338 |
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### Framework versions
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- Transformers 4.45.1
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- Pytorch 2.4.0
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- Datasets 3.0.1
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- Tokenizers 0.20.0
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generation_config.json
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{
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"bad_words_ids": [
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[
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60446
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]
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],
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"bos_token_id": 0,
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"decoder_start_token_id": 60446,
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"eos_token_id": 0,
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"forced_eos_token_id": 0,
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"max_length": 512,
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"num_beams": 6,
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"pad_token_id": 60446,
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"renormalize_logits": true,
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"transformers_version": "4.45.1"
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}
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