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1_Pooling/config.json

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+{
+  "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
+}

README.md

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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 'lymphocyte activation: Morphologic alteration of small B LYMPHOCYTES or T
+    LYMPHOCYTES in culture into large blast-like cells able to synthesize DNA and
+    RNA and to divide mitotically. It is induced by INTERLEUKINS; MITOGENS such as
+    PHYTOHEMAGGLUTININS, and by specific ANTIGENS. It may also occur in vivo as in
+    GRAFT REJECTION.'
+- text: 'burns: Injuries to tissues caused by contact with heat, steam, chemicals
+    (BURNS, CHEMICAL), electricity (BURNS, ELECTRIC), or the like.'
+- text: 'solutions: "The homogeneous mixtures formed by the mixing of a solid, liquid,
+    or gaseous substance (solute) with a liquid (the solvent), from which the dissolved
+    substances can be recovered by physical processes. (From Grant & Hackhs Chemical
+    Dictionary, 5th ed)"'
+- text: 'tooth discoloration: Any change in the hue, color, or translucency of a tooth
+    due to any cause. Restorative filling materials, drugs (both topical and systemic),
+    pulpal necrosis, or hemorrhage may be responsible. (Jablonski, Dictionary of Dentistry,
+    1992, p253)'
+- text: 'foreign-body reaction: Chronic inflammation and granuloma formation around
+    irritating foreign bodies.'
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: false
+base_model: cambridgeltl/SapBERT-from-PubMedBERT-fulltext
+model-index:
+- name: SetFit with cambridgeltl/SapBERT-from-PubMedBERT-fulltext
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.5240963855421686
+      name: Accuracy
+---
+
+# SetFit with cambridgeltl/SapBERT-from-PubMedBERT-fulltext
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext)
+- **Classification head:** a OneVsRestClassifier instance
+- **Maximum Sequence Length:** 512 tokens
+<!-- - **Number of Classes:** Unknown -->
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.5241   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("setfit_model_id")
+# Run inference
+preds = model("foreign-body reaction: Chronic inflammation and granuloma formation around irritating foreign bodies.")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### 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 Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 2   | 30.4473 | 134 |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0003 | 1    | 0.2929        | -               |
+| 0.0136 | 50   | 0.2377        | -               |
+| 0.0272 | 100  | 0.2321        | -               |
+| 0.0408 | 150  | 0.2199        | -               |
+| 0.0544 | 200  | 0.1726        | -               |
+| 0.0680 | 250  | 0.1355        | -               |
+| 0.0816 | 300  | 0.1207        | -               |
+| 0.0952 | 350  | 0.1138        | -               |
+| 0.1088 | 400  | 0.1154        | -               |
+| 0.1223 | 450  | 0.095         | -               |
+| 0.1359 | 500  | 0.106         | -               |
+| 0.1495 | 550  | 0.0913        | -               |
+| 0.1631 | 600  | 0.0943        | -               |
+| 0.1767 | 650  | 0.0974        | -               |
+| 0.1903 | 700  | 0.0923        | -               |
+| 0.2039 | 750  | 0.0893        | -               |
+| 0.2175 | 800  | 0.0804        | -               |
+| 0.2311 | 850  | 0.0849        | -               |
+| 0.2447 | 900  | 0.0766        | -               |
+| 0.2583 | 950  | 0.0838        | -               |
+| 0.2719 | 1000 | 0.0725        | -               |
+| 0.2855 | 1050 | 0.073         | -               |
+| 0.2991 | 1100 | 0.055         | -               |
+| 0.3127 | 1150 | 0.0758        | -               |
+| 0.3263 | 1200 | 0.0709        | -               |
+| 0.3399 | 1250 | 0.0569        | -               |
+| 0.3535 | 1300 | 0.0535        | -               |
+| 0.3670 | 1350 | 0.0557        | -               |
+| 0.3806 | 1400 | 0.0596        | -               |
+| 0.3942 | 1450 | 0.0453        | -               |
+| 0.4078 | 1500 | 0.0428        | -               |
+| 0.4214 | 1550 | 0.0482        | -               |
+| 0.4350 | 1600 | 0.0465        | -               |
+| 0.4486 | 1650 | 0.0469        | -               |
+| 0.4622 | 1700 | 0.0479        | -               |
+| 0.4758 | 1750 | 0.0451        | -               |
+| 0.4894 | 1800 | 0.0613        | -               |
+| 0.5030 | 1850 | 0.0533        | -               |
+| 0.5166 | 1900 | 0.0476        | -               |
+| 0.5302 | 1950 | 0.0449        | -               |
+| 0.5438 | 2000 | 0.0543        | -               |
+| 0.5574 | 2050 | 0.0509        | -               |
+| 0.5710 | 2100 | 0.043         | -               |
+| 0.5846 | 2150 | 0.0482        | -               |
+| 0.5982 | 2200 | 0.0513        | -               |
+| 0.6117 | 2250 | 0.0366        | -               |
+| 0.6253 | 2300 | 0.0385        | -               |
+| 0.6389 | 2350 | 0.0446        | -               |
+| 0.6525 | 2400 | 0.0411        | -               |
+| 0.6661 | 2450 | 0.037         | -               |
+| 0.6797 | 2500 | 0.0321        | -               |
+| 0.6933 | 2550 | 0.0468        | -               |
+| 0.7069 | 2600 | 0.0331        | -               |
+| 0.7205 | 2650 | 0.0315        | -               |
+| 0.7341 | 2700 | 0.0435        | -               |
+| 0.7477 | 2750 | 0.0394        | -               |
+| 0.7613 | 2800 | 0.0381        | -               |
+| 0.7749 | 2850 | 0.0418        | -               |
+| 0.7885 | 2900 | 0.0347        | -               |
+| 0.8021 | 2950 | 0.0468        | -               |
+| 0.8157 | 3000 | 0.0352        | -               |
+| 0.8293 | 3050 | 0.0416        | -               |
+| 0.8428 | 3100 | 0.0354        | -               |
+| 0.8564 | 3150 | 0.0329        | -               |
+| 0.8700 | 3200 | 0.0359        | -               |
+| 0.8836 | 3250 | 0.036         | -               |
+| 0.8972 | 3300 | 0.0362        | -               |
+| 0.9108 | 3350 | 0.0296        | -               |
+| 0.9244 | 3400 | 0.041         | -               |
+| 0.9380 | 3450 | 0.0375        | -               |
+| 0.9516 | 3500 | 0.0282        | -               |
+| 0.9652 | 3550 | 0.0341        | -               |
+| 0.9788 | 3600 | 0.0283        | -               |
+| 0.9924 | 3650 | 0.0339        | -               |
+
+### Framework Versions
+- Python: 3.11.11
+- SetFit: 1.1.1
+- Sentence Transformers: 3.4.1
+- Transformers: 4.50.0
+- PyTorch: 2.6.0+cu124
+- Datasets: 3.4.1
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## 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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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.50.0",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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+oid sha256:11551bb0e8b36527ccbb9c561b54ed3f3a59e124e3a194baae2c942e1e4c4fa0
+size 437951328

model_head.pkl

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+oid sha256:cf59c6868d5270009202f730a22f0b7bd15a3a94cc4fa69578c6cefba8d421bc
+size 150052

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
+}

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "3": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "full_tokenizer_file": null,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}