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---
base_model: distilbert/distilbert-base-multilingual-cased
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:654495
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: সম্পূৰ্ণৰূপে ভিন্ন ধৰণৰ পেৰাচুট আৰু এটা উড়ন্ত পক্ষীৰ মাজত, আহ্,
শব্দৰ তিনিগুণ বেগত, ঘণ্টাৰ ২২, ০০০ মাইলত।
sentences:
- ঘণ্টাৰ ২০, ০০০ কিলোমিটাৰতকৈ অধিক গতিত উড়ে।
- মোৰ ঘৰত দুটা কম্পিউটাৰ আছে।
- সকলো ক্ৰীড়াৰ নাম ক্ৰীড়াত ব্যৱহাৰ কৰা এটা সঁজুলিৰ নামেৰে নামকৰণ কৰা হয়।
- source_sentence: আৰু তাৰ পিছত মই তেওঁক যাবলৈ শুনিছিলোঁ, সেয়েহে মই এতিয়াও মোৰ কাম
শেষ কৰি আছো।
sentences:
- মই আজি যিটো কৰিব লাগিব সেয়া কৰি আছো।
- '"Bato (বা" "vato" ") এটা স্পেনিছ শব্দ যাৰ অৰ্থ হৈছে" "পুৰুষ" "বা" "বন্ধু" "।"'
- পিতৃ-মাতৃয়ে ঘৰত থাকিল।
- source_sentence: মই কেৱল বুজাবলৈ চেষ্টা কৰিছিলোঁ।
sentences:
- মই বুজিবলৈ চেষ্টা কৰিছিলোঁ।
- মই আন কেইবাটাও প্ৰস্তাৱ দিবলৈ আহিছিলোঁ।
- প্ৰেমিক নামৰ এজন খেতিয়কে নিজৰ হত্যাৰ আঁচনি তৈয়াৰ কৰোতে ঘাসপূৰ্ণ স্থানত লুকুৱাই
থৈ যায়।
- source_sentence: আৰু, উম, যদি এইটো বাঢ়ি আহিব আৰু কেৱল বাঢ়ি আহিব তেতিয়াহ 'লে'
whish 'হ' ব, আৰু যেনেকৈ ই আপোনাৰ মূৰটো বন্ধ কৰি দিব।
sentences:
- প্ৰাৰম্ভিক শিক্ষা লাভ কৰা আৰু বয়সস্থ ল 'ৰা-ছোৱালীয়ে প্ৰায়ে ভৱিষ্যতৰ বিষয়ে
সপোন দেখে।
- তেওঁলোকে মোৰ ওচৰলৈ কিয় আহিছে বুলি প্ৰশ্ন কৰিলে।
- যদি কোনো ধৰণৰ পৰিৱৰ্তন হয়, তেনেহ 'লে তাৰ লগত এক শব্দ বাঢ়িব পাৰে।
- source_sentence: মই ভালদৰে জানিব নোৱাৰোঁ আপোনালোকৰ সৈতে কথা বতৰা আৰু এক ভাল সন্ধ্যা
আছিল
sentences:
- মই নিশ্চিত নহয় কিন্তু মই অলপ ভাল, আজি ৰাতি আপোনালোকৰ সৈতে কথা পাতিবলৈ পাই ভাল
লাগিল।
- Shannon এ বাৰ্তা উপেক্ষা কৰিছে।
- মানুহজনে ষ্টক এক্সচেঞ্জত লেনদেনৰ বিষয়ে জানিবলৈ চেষ্টা কৰিছিল।
model-index:
- name: SentenceTransformer based on distilbert/distilbert-base-multilingual-cased
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: pritamdeka/stsb assamese translated dev
type: pritamdeka/stsb-assamese-translated-dev
metrics:
- type: pearson_cosine
value: 0.7169579983340281
name: Pearson Cosine
- type: spearman_cosine
value: 0.7220987460972806
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7380110422340219
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7452082040848071
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7386577662108481
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7458961406429292
name: Spearman Euclidean
- type: pearson_dot
value: 0.6480820840127198
name: Pearson Dot
- type: spearman_dot
value: 0.6478256799308721
name: Spearman Dot
- type: pearson_max
value: 0.7386577662108481
name: Pearson Max
- type: spearman_max
value: 0.7458961406429292
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: pritamdeka/stsb assamese translated test
type: pritamdeka/stsb-assamese-translated-test
metrics:
- type: pearson_cosine
value: 0.656822131496386
name: Pearson Cosine
- type: spearman_cosine
value: 0.6621886312595516
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6675496858061083
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6722470705036974
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6681862838868354
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6727345795749732
name: Spearman Euclidean
- type: pearson_dot
value: 0.5691955650489428
name: Pearson Dot
- type: spearman_dot
value: 0.570867962692759
name: Spearman Dot
- type: pearson_max
value: 0.6681862838868354
name: Pearson Max
- type: spearman_max
value: 0.6727345795749732
name: Spearman Max
---
# SentenceTransformer based on distilbert/distilbert-base-multilingual-cased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) <!-- at revision 45c032ab32cc946ad88a166f7cb282f58c753c2e -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1")
# Run inference
sentences = [
'মই ভালদৰে জানিব নোৱাৰোঁ আপোনালোকৰ সৈতে কথা বতৰা আৰু এক ভাল সন্ধ্যা আছিল',
'মই নিশ্চিত নহয় কিন্তু মই অলপ ভাল, আজি ৰাতি আপোনালোকৰ সৈতে কথা পাতিবলৈ পাই ভাল লাগিল।',
'Shannon এ বাৰ্তা উপেক্ষা কৰিছে।',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `pritamdeka/stsb-assamese-translated-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.717 |
| **spearman_cosine** | **0.7221** |
| pearson_manhattan | 0.738 |
| spearman_manhattan | 0.7452 |
| pearson_euclidean | 0.7387 |
| spearman_euclidean | 0.7459 |
| pearson_dot | 0.6481 |
| spearman_dot | 0.6478 |
| pearson_max | 0.7387 |
| spearman_max | 0.7459 |
#### Semantic Similarity
* Dataset: `pritamdeka/stsb-assamese-translated-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6568 |
| **spearman_cosine** | **0.6622** |
| pearson_manhattan | 0.6675 |
| spearman_manhattan | 0.6722 |
| pearson_euclidean | 0.6682 |
| spearman_euclidean | 0.6727 |
| pearson_dot | 0.5692 |
| spearman_dot | 0.5709 |
| pearson_max | 0.6682 |
| spearman_max | 0.6727 |
<!--
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## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | pritamdeka/stsb-assamese-translated-dev_spearman_cosine | pritamdeka/stsb-assamese-translated-test_spearman_cosine |
|:----------:|:---------:|:-------------:|:----------:|:-------------------------------------------------------:|:--------------------------------------------------------:|
| 0 | 0 | - | - | 0.5489 | - |
| 0.0489 | 500 | 1.9387 | 1.7308 | 0.6808 | - |
| 0.0978 | 1000 | 1.0503 | 1.7373 | 0.6689 | - |
| 0.1467 | 1500 | 0.92 | 1.5838 | 0.6761 | - |
| 0.1956 | 2000 | 0.8754 | 1.4807 | 0.6518 | - |
| 0.2445 | 2500 | 0.7988 | 1.3797 | 0.6853 | - |
| 0.2933 | 3000 | 0.7606 | 1.3713 | 0.7108 | - |
| 0.3422 | 3500 | 0.7228 | 1.2510 | 0.6677 | - |
| 0.3911 | 4000 | 0.688 | 1.2374 | 0.6734 | - |
| 0.4400 | 4500 | 0.6992 | 1.2173 | 0.6891 | - |
| 0.4889 | 5000 | 0.6108 | 1.1638 | 0.7017 | - |
| 0.5378 | 5500 | 0.612 | 1.0815 | 0.7102 | - |
| 0.5867 | 6000 | 0.6259 | 1.0664 | 0.7202 | - |
| 0.6356 | 6500 | 0.5863 | 1.0464 | 0.7047 | - |
| 0.6845 | 7000 | 0.5941 | 1.0111 | 0.7101 | - |
| 0.7334 | 7500 | 0.5436 | 1.0023 | 0.7171 | - |
| 0.7822 | 8000 | 0.555 | 0.9633 | 0.7202 | - |
| 0.8311 | 8500 | 0.5466 | 0.9651 | 0.7279 | - |
| 0.8800 | 9000 | 0.5326 | 0.9611 | 0.7262 | - |
| 0.9289 | 9500 | 0.5055 | 0.9313 | 0.7276 | - |
| **0.9778** | **10000** | **0.4828** | **0.9172** | **0.7221** | **-** |
| 1.0 | 10227 | - | - | - | 0.6622 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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