Add new SentenceTransformer model

1_Pooling/config.json

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+{
+    "word_embedding_dimension": 768,
+    "pooling_mode_cls_token": true,
+    "pooling_mode_mean_tokens": false,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:27120
+- loss:ContrastiveLoss
+base_model: cambridgeltl/SapBERT-from-PubMedBERT-fulltext
+widget:
+- source_sentence: 'anencephaly [SEP] Sequential observations of exencephaly and subsequent
+    morphological changes by mouse exo utero development system: analysis of t'
+  sentences:
+  - Hemostatic Disorders [SEP] Pathological processes involving the integrity of blood
+    circulation. Hemostasis depends on the integrity of BLOOD VESSELS, blood
+  - Pentylenetetrazole [SEP] A pharmaceutical agent that displays activity as a central
+    nervous system and respiratory stimulant. It is considered a non-comp
+  - Epilepsy [SEP] A disorder characterized by recurrent episodes of paroxysmal brain
+    dysfunction due to a sudden, disorderly, and excessive neuron
+- source_sentence: nifedipine [SEP] The effect of nifedipine on renal function in
+    liver transplant recipients who were receiving tacrolimus was evaluated between
+    Ja
+  sentences:
+  - 'Nifedipine [SEP] A potent vasodilator agent with calcium antagonistic action.
+    It is a useful anti-anginal agent that also lowers blood pressure.
+
+    '
+  - Hypotension [SEP] Abnormally low BLOOD PRESSURE that can result in inadequate
+    blood flow to the brain and other vital organs. Common symptom is DI
+  - Granulomatosis with Polyangiitis [SEP] A multisystemic disease of a complex genetic
+    background. It is characterized by inflammation of the blood vessels (VASCULITIS)
+    l
+- source_sentence: toxicity [SEP] Effects of calcium channel blockers on bupivacaine-induced
+    toxicity.
+  sentences:
+  - 'Methamphetamine [SEP] A central nervous system stimulant and sympathomimetic
+    with actions and uses similar to DEXTROAMPHETAMINE. The smokable form is '
+  - Dizocilpine Maleate [SEP] A potent noncompetitive antagonist of the NMDA receptor
+    (RECEPTORS, N-METHYL-D-ASPARTATE) used mainly as a research tool. The dr
+  - Hallucinations [SEP] Subjectively experienced sensations in the absence of an
+    appropriate stimulus, but which are regarded by the individual as real.
+- source_sentence: Ca [SEP] Interactive effects of variations in [Na]o and [Ca]o on
+    rat atrial spontaneous frequency.
+  sentences:
+  - Brain Edema [SEP] Increased intracellular or extracellular fluid in brain tissue.
+    Cytotoxic brain edema (swelling due to increased intracellular f
+  - "Capsaicin [SEP] An alkylamide found in CAPSICUM that acts at TRPV CATION CHANNELS.\n\
+    \    "
+  - "Thrombocytopenia [SEP] A subnormal level of BLOOD PLATELETS.\n    "
+- source_sentence: acromegaly [SEP] This article reports the changes in gallbladder
+    function examined by ultrasonography in 20 Chinese patients with active acromega
+  sentences:
+  - "Indocyanine Green [SEP] A tricarbocyanine dye that is used diagnostically in\
+    \ liver function tests and to determine blood volume and cardiac output.\n    "
+  - Nitroglycerin [SEP] A volatile vasodilator which relieves ANGINA PECTORIS by stimulating
+    GUANYLATE CYCLASE and lowering cytosolic calcium. It is als
+  - Nausea [SEP] An unpleasant sensation in the stomach usually accompanied by the
+    urge to vomit. Common causes are early pregnancy, sea and moti
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on cambridgeltl/SapBERT-from-PubMedBERT-fulltext
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext). 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:** [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext) <!-- at revision 090663c3ae57bf35ffe4d0d468a2a88d03051a4d -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 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/huggingface/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, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("Stevenf232/context_fine-tuned-SapBERT")
+# Run inference
+sentences = [
+    'acromegaly [SEP] This article reports the changes in gallbladder function examined by ultrasonography in 20 Chinese patients with active acromega',
+    'Nitroglycerin [SEP] A volatile vasodilator which relieves ANGINA PECTORIS by stimulating GUANYLATE CYCLASE and lowering cytosolic calcium. It is als',
+    'Indocyanine Green [SEP] A tricarbocyanine dye that is used diagnostically in liver function tests and to determine blood volume and cardiac output.\n    ',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.2218, 0.3974],
+#         [0.2218, 1.0000, 0.5304],
+#         [0.3974, 0.5304, 1.0000]])
+```
+
+<!--
+### 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.*
+-->
+
+<!--
+## 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: 27,120 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 27.48 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.74 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.21</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                              | sentence_1                                                                                                                                                                | label            |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>toxic to the central nervous system [SEP] Treatment for scabies is usually initiated by general practitioners; most consider lindane (gamma benzene hexachloride) the trea</code> | <code>Thyrotoxicosis [SEP] A hypermetabolic syndrome caused by excess THYROID HORMONES which may come from endogenous or exogenous sources. The endogenous </code>        | <code>0.0</code> |
+  | <code>cancer [SEP] Doxorubicin is an effective anticancer chemotherapeutic agent known to cause acute and chronic cardiomyopathy. To develop a more</code>                              | <code>Nystagmus, Pathologic [SEP] Involuntary movements of the eye that are divided into two types, jerk and pendular. Jerk nystagmus has a slow phase in one dire</code> | <code>0.0</code> |
+  | <code>doxorubicin [SEP] Doxorubicin is an effective anticancer chemotherapeutic agent known to cause acute and chronic cardiomyopathy. To develop a more</code>                         | <code>Cisplatin [SEP] An inorganic and water-soluble platinum complex. After undergoing hydrolysis, it reacts with DNA to produce both intra and inter</code>             | <code>0.0</code> |
+* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
+      "margin": 0.5,
+      "size_average": true
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `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>
+
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_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`: None
+- `warmup_ratio`: None
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `enable_jit_checkpoint`: False
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `use_cpu`: False
+- `seed`: 42
+- `data_seed`: None
+- `bf16`: False
+- `fp16`: True
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: -1
+- `ddp_backend`: None
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `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
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_num_input_tokens_seen`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `use_cache`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.2950 | 500  | 0.0106        |
+| 0.5900 | 1000 | 0.0065        |
+| 0.8850 | 1500 | 0.0058        |
+| 1.1799 | 2000 | 0.0045        |
+| 1.4749 | 2500 | 0.0038        |
+| 1.7699 | 3000 | 0.0036        |
+| 2.0649 | 3500 | 0.0036        |
+| 2.3599 | 4000 | 0.0027        |
+| 2.6549 | 4500 | 0.0027        |
+| 2.9499 | 5000 | 0.0027        |
+
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.2.3
+- Transformers: 5.0.0
+- PyTorch: 2.9.0+cu128
+- Accelerate: 1.12.0
+- Datasets: 4.0.0
+- Tokenizers: 0.22.2
+
+## 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",
+}
+```
+
+#### ContrastiveLoss
+```bibtex
+@inproceedings{hadsell2006dimensionality,
+    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
+    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
+    title={Dimensionality Reduction by Learning an Invariant Mapping},
+    year={2006},
+    volume={2},
+    number={},
+    pages={1735-1742},
+    doi={10.1109/CVPR.2006.100}
+}
+```
+
+<!--
+## 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.*
+-->

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.2.3",
+    "transformers": "5.0.0",
+    "pytorch": "2.9.0+cu128"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:255e4f05ca51e18b602ea616a83cf5894df0528cbd1f94f8f8c988a23355494e
+size 437951304

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "full_tokenizer_file": null,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}