| --- |
| base_model: l3cube-pune/assamese-bert |
| datasets: |
| - sentence-transformers/all-nli |
| language: |
| - en |
| 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:557850 |
| - loss:MultipleNegativesRankingLoss |
| widget: |
| - source_sentence: A man is jumping unto his filthy bed. |
| sentences: |
| - A young male is looking at a newspaper while 2 females walks past him. |
| - The bed is dirty. |
| - The man is on the moon. |
| - source_sentence: A carefully balanced male stands on one foot near a clean ocean |
| beach area. |
| sentences: |
| - A man is ouside near the beach. |
| - Three policemen patrol the streets on bikes |
| - A man is sitting on his couch. |
| - source_sentence: The man is wearing a blue shirt. |
| sentences: |
| - Near the trashcan the man stood and smoked |
| - A man in a blue shirt leans on a wall beside a road with a blue van and red car |
| with water in the background. |
| - A man in a black shirt is playing a guitar. |
| - source_sentence: The girls are outdoors. |
| sentences: |
| - Two girls riding on an amusement part ride. |
| - a guy laughs while doing laundry |
| - Three girls are standing together in a room, one is listening, one is writing |
| on a wall and the third is talking to them. |
| - source_sentence: A construction worker peeking out of a manhole while his coworker |
| sits on the sidewalk smiling. |
| sentences: |
| - A worker is looking out of a manhole. |
| - A man is giving a presentation. |
| - The workers are both inside the manhole. |
| model-index: |
| - name: SentenceTransformer based on l3cube-pune/assamese-bert |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts dev |
| type: sts-dev |
| metrics: |
| - type: pearson_cosine |
| value: 0.8448431188558219 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.848270397607023 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.8429962459024234 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.8461225961159852 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.8450811877325317 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.8481702238714027 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.7600437454974306 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.7604490741243843 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.8450811877325317 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.848270397607023 |
| name: Spearman Max |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts test |
| type: sts-test |
| metrics: |
| - type: pearson_cosine |
| value: 0.8160018744466311 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.8230016183156494 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.8104201802445242 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.8104000391884387 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.8108715587588242 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.8112881633291651 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.7088828153549986 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.6991542788989243 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.8160018744466311 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.8230016183156494 |
| name: Spearman Max |
| --- |
| |
| # SentenceTransformer based on l3cube-pune/assamese-bert |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [l3cube-pune/assamese-bert](https://huggingface.co/l3cube-pune/assamese-bert) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** [l3cube-pune/assamese-bert](https://huggingface.co/l3cube-pune/assamese-bert) <!-- at revision ebe759281276a70717fd8d63102a9820b9360812 --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 768 tokens |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) |
| - **Language:** en |
| <!-- - **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: BertModel |
| (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/assamese-bert-nli-v2") |
| # Run inference |
| sentences = [ |
| 'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.', |
| 'A worker is looking out of a manhole.', |
| 'The workers are both inside the manhole.', |
| ] |
| 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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Semantic Similarity |
| * Dataset: `sts-dev` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | pearson_cosine | 0.8448 | |
| | **spearman_cosine** | **0.8483** | |
| | pearson_manhattan | 0.843 | |
| | spearman_manhattan | 0.8461 | |
| | pearson_euclidean | 0.8451 | |
| | spearman_euclidean | 0.8482 | |
| | pearson_dot | 0.76 | |
| | spearman_dot | 0.7604 | |
| | pearson_max | 0.8451 | |
| | spearman_max | 0.8483 | |
| |
| #### Semantic Similarity |
| * Dataset: `sts-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
| |
| | Metric | Value | |
| |:--------------------|:----------| |
| | pearson_cosine | 0.816 | |
| | **spearman_cosine** | **0.823** | |
| | pearson_manhattan | 0.8104 | |
| | spearman_manhattan | 0.8104 | |
| | pearson_euclidean | 0.8109 | |
| | spearman_euclidean | 0.8113 | |
| | pearson_dot | 0.7089 | |
| | spearman_dot | 0.6992 | |
| | pearson_max | 0.816 | |
| | spearman_max | 0.823 | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### sentence-transformers/all-nli |
| |
| * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
| * Size: 557,850 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 7 tokens</li><li>mean: 10.55 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.08 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.7 tokens</li><li>max: 53 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| |
| | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> | |
| | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> | |
| | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim" |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### sentence-transformers/all-nli |
| |
| * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
| * Size: 6,584 evaluation samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 18.54 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.97 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.59 tokens</li><li>max: 29 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| |
| | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> | |
| | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> | |
| | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim" |
| } |
| ``` |
| |
| ### 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 |
| - `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`: False |
| - `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 | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
| |:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| |
| | 0 | 0 | - | - | 0.6401 | - | |
| | 0.0574 | 500 | 2.5567 | 1.2774 | 0.7654 | - | |
| | 0.1147 | 1000 | 1.3874 | 1.0303 | 0.7997 | - | |
| | 0.1721 | 1500 | 1.1493 | 0.9597 | 0.7867 | - | |
| | 0.2294 | 2000 | 0.9885 | 0.7656 | 0.7895 | - | |
| | 0.2868 | 2500 | 0.9588 | 0.8041 | 0.7797 | - | |
| | 0.3442 | 3000 | 0.922 | 0.7280 | 0.7785 | - | |
| | 0.4015 | 3500 | 0.8693 | 0.6803 | 0.7925 | - | |
| | 0.4589 | 4000 | 0.8436 | 0.6892 | 0.7866 | - | |
| | 0.5162 | 4500 | 0.8033 | 0.7127 | 0.7818 | - | |
| | 0.5736 | 5000 | 0.8061 | 0.6854 | 0.7746 | - | |
| | 0.6310 | 5500 | 0.8069 | 0.6496 | 0.7856 | - | |
| | 0.6883 | 6000 | 0.8133 | 0.6490 | 0.7787 | - | |
| | 0.7457 | 6500 | 0.7857 | 0.5926 | 0.8010 | - | |
| | 0.8030 | 7000 | 0.4404 | 0.4472 | 0.8457 | - | |
| | 0.8604 | 7500 | 0.3422 | 0.4441 | 0.8473 | - | |
| | 0.9177 | 8000 | 0.308 | 0.4315 | 0.8494 | - | |
| | 0.9751 | 8500 | 0.299 | 0.4305 | 0.8483 | - | |
| | 1.0 | 8717 | - | - | - | 0.8230 | |
| |
| |
| ### 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} |
| } |
| ``` |
| |
| <!-- |
| ## Glossary |
| |
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
| |
| <!-- |
| ## Model Card Authors |
| |
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
| |
| <!-- |
| ## Model Card Contact |
| |
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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