| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:47610 |
| - loss:MultipleNegativesRankingLoss |
| widget: |
| - source_sentence: '[MENTION] Gustavus And Louise Pfeiffer Research Foundation [CITY] |
| Bangor [COUNTRY] United States' |
| sentences: |
| - '[MENTION] Gustavus And Louise Pfeiffer Research Foundation [CITY] Bangor [COUNTRY] |
| United States' |
| - '[MENTION] Fifth Tianjin Central Hospital [CITY] Tianjin [COUNTRY] China' |
| - '[MENTION] Purdue Research Foundation [ACRONYM] PRF [CITY] West Lafayette [COUNTRY] |
| United States' |
| - source_sentence: '[MENTION] ইন্টার-ইউরিভার্সিটি সেন্টার ফর অ্যাস্ট্রোনমি অ্যান্ড |
| অ্যাস্ট্রোফিজিক্স [CITY] Pune [COUNTRY] India' |
| sentences: |
| - '[MENTION] National Centre for Radio Astrophysics [ACRONYM] NCRA TIFR [PARENT] |
| Tata Institute of Fundamental Research [ACRONYM] TIFR [CITY] Pune [COUNTRY] India' |
| - '[MENTION] Inter-University Centre for Astronomy and Astrophysics [ACRONYM] IUCAA |
| [CITY] Pune [COUNTRY] India' |
| - '[MENTION] Iskra Medical (Slovenia) [CITY] Radovljica [COUNTRY] Slovenia' |
| - source_sentence: '[MENTION] Raytheon Technologies (Canada) [CITY] Calgary [COUNTRY] |
| Canada' |
| sentences: |
| - '[MENTION] Raytheon Technologies (Canada) [ACRONYM] RCL [PARENT] RTX (United States) |
| [CITY] Calgary [COUNTRY] Canada' |
| - '[MENTION] Yunnan Open University [CITY] Kunming [COUNTRY] China' |
| - '[MENTION] ATCO (Canada) [CITY] Calgary [COUNTRY] Canada' |
| - source_sentence: '[MENTION] 유한양행 [CITY] Seoul' |
| sentences: |
| - '[MENTION] Instituto de Medicina Molecular João Lobo Antunes [ACRONYM] IMM [PARENT] |
| University of Lisbon [CITY] Lisbon [COUNTRY] Portugal' |
| - '[MENTION] Boehringer Ingelheim (South Korea) [PARENT] Boehringer Ingelheim (Germany) |
| [CITY] Seoul [COUNTRY] South Korea' |
| - '[MENTION] Yuhan (South Korea) [CITY] Seoul [COUNTRY] South Korea' |
| - source_sentence: '[MENTION] Hyderabad Cleft Society [COUNTRY] India' |
| sentences: |
| - '[MENTION] Hyderabad Cleft Society [ACRONYM] HCS [CITY] Hyderabad [COUNTRY] India' |
| - '[MENTION] Hyderabad Rheumatology Center [ACRONYM] HRC [CITY] Hyderabad [COUNTRY] |
| India' |
| - '[MENTION] National Institute of Technology Akita College [PARENT] National Institute |
| of Technology [CITY] Akita [COUNTRY] Japan' |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| model-index: |
| - name: SentenceTransformer |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: entity linking eval |
| type: entity_linking_eval |
| metrics: |
| - type: pearson_cosine |
| value: 0.7072780089709011 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.6825742231480432 |
| name: Spearman Cosine |
| --- |
| |
| # SentenceTransformer |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-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:** [Unknown](https://huggingface.co/unknown) --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 1024 dimensions |
| - **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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
| (1): Pooling({'word_embedding_dimension': 1024, '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}) |
| (2): Normalize() |
| ) |
| ``` |
|
|
| ## 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("SIRIS-Lab/affilgood-dense-retriever") |
| # Run inference |
| sentences = [ |
| '[MENTION] Hyderabad Cleft Society [COUNTRY] India', |
| '[MENTION] Hyderabad Cleft Society [ACRONYM] HCS [CITY] Hyderabad [COUNTRY] India', |
| '[MENTION] Hyderabad Rheumatology Center [ACRONYM] HRC [CITY] Hyderabad [COUNTRY] India', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 1024] |
| |
| # 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: `entity_linking_eval` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | pearson_cosine | 0.7073 | |
| | **spearman_cosine** | **0.6826** | |
| |
| <!-- |
| ## 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 |
| |
| #### Unnamed Dataset |
| |
| * Size: 47,610 training samples |
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence_0 | sentence_1 | sentence_2 | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 13.61 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 18.63 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 19.66 tokens</li><li>max: 58 tokens</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | sentence_2 | |
| |:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>[MENTION] The Prince Of Wales'S Institute Of Architecture [CITY] London [COUNTRY] United Kingdom</code> | <code>[MENTION] The Princes Foundation [CITY] London [COUNTRY] United Kingdom</code> | <code>[MENTION] Royal Institute of British Architects [ACRONYM] RIBA [CITY] London [COUNTRY] United Kingdom</code> | |
| | <code>[MENTION] Development Finance & Public Policies [COUNTRY] Belgium</code> | <code>[MENTION] Development Finance and Public Policies [ACRONYM] DEFIPP [PARENT] University of Namur [CITY] Namur [COUNTRY] Belgium</code> | <code>[MENTION] Service Public Federal Finances [ACRONYM] SPF [CITY] Brussels [COUNTRY] Belgium</code> | |
| | <code>[MENTION] EES [COUNTRY] United States</code> | <code>[MENTION] Emerald Education Systems [ACRONYM] EES [CITY] Pasadena [COUNTRY] United States</code> | <code>[MENTION] ESI Group (United States) [ACRONYM] ESI [PARENT] ESI Group (France) [ACRONYM] ESI [CITY] Farmington Hills [COUNTRY] United States</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`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `fp16`: True |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### 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`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `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 |
| - `num_train_epochs`: 3 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: {} |
| - `warmup_ratio`: 0.0 |
| - `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 |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | entity_linking_eval_spearman_cosine | |
| |:------:|:----:|:-------------:|:-----------------------------------:| |
| | 0.1680 | 500 | 0.3431 | - | |
| | 0.3360 | 1000 | 0.252 | 0.4769 | |
| | 0.5040 | 1500 | 0.291 | - | |
| | 0.6720 | 2000 | 0.2445 | 0.6494 | |
| | 0.8401 | 2500 | 0.2339 | - | |
| | 1.0 | 2976 | - | 0.6694 | |
| | 1.0081 | 3000 | 0.2256 | 0.6730 | |
| | 1.1761 | 3500 | 0.16 | - | |
| | 1.3441 | 4000 | 0.1428 | 0.6750 | |
| | 1.5121 | 4500 | 0.1661 | - | |
| | 1.6801 | 5000 | 0.139 | 0.6713 | |
| | 1.8481 | 5500 | 0.1408 | - | |
| | 2.0 | 5952 | - | 0.6768 | |
| | 2.0161 | 6000 | 0.1409 | 0.6763 | |
| | 2.1841 | 6500 | 0.0759 | - | |
| | 2.3522 | 7000 | 0.0702 | 0.6820 | |
| | 2.5202 | 7500 | 0.0716 | - | |
| | 2.6882 | 8000 | 0.0777 | 0.6805 | |
| | 2.8562 | 8500 | 0.0685 | - | |
| | 3.0 | 8928 | - | 0.6826 | |
| |
| |
| ### Framework Versions |
| - Python: 3.10.12 |
| - Sentence Transformers: 3.4.1 |
| - Transformers: 4.41.2 |
| - PyTorch: 2.2.0+cu121 |
| - Accelerate: 1.2.1 |
| - Datasets: 2.18.0 |
| - 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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