Upload folder using huggingface_hub

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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

@@ -0,0 +1,250 @@
+---
+base_model: cambridgeltl/SapBERT-from-PubMedBERT-fulltext
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: exorphins
+- text: phosphatidylethanolamines
+- text: lipopolysaccharides
+- text: ion channels
+- text: caspases
+inference: false
+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: 0.17570754716981132
+      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 MultiOutputClassifier 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 MultiOutputClassifier 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** | 0.1757   |
+
+## 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("caspases")
+```
+
+<!--
+### 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   | 1   | 1.7652 | 5   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (3, 3)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 15
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0009 | 1    | 0.2361        | -               |
+| 0.0463 | 50   | 0.2377        | -               |
+| 0.0927 | 100  | 0.2269        | -               |
+| 0.1390 | 150  | 0.2104        | -               |
+| 0.1854 | 200  | 0.1871        | -               |
+| 0.2317 | 250  | 0.1437        | -               |
+| 0.2780 | 300  | 0.1322        | -               |
+| 0.3244 | 350  | 0.1365        | -               |
+| 0.3707 | 400  | 0.1155        | -               |
+| 0.4171 | 450  | 0.1144        | -               |
+| 0.4634 | 500  | 0.1068        | -               |
+| 0.5097 | 550  | 0.1011        | -               |
+| 0.5561 | 600  | 0.095         | -               |
+| 0.6024 | 650  | 0.0933        | -               |
+| 0.6487 | 700  | 0.1063        | -               |
+| 0.6951 | 750  | 0.0999        | -               |
+| 0.7414 | 800  | 0.0823        | -               |
+| 0.7878 | 850  | 0.0877        | -               |
+| 0.8341 | 900  | 0.0767        | -               |
+| 0.8804 | 950  | 0.0849        | -               |
+| 0.9268 | 1000 | 0.0796        | -               |
+| 0.9731 | 1050 | 0.0877        | -               |
+| 1.0195 | 1100 | 0.0759        | -               |
+| 1.0658 | 1150 | 0.0705        | -               |
+| 1.1121 | 1200 | 0.0728        | -               |
+| 1.1585 | 1250 | 0.0738        | -               |
+| 1.2048 | 1300 | 0.0767        | -               |
+| 1.2512 | 1350 | 0.0692        | -               |
+| 1.2975 | 1400 | 0.0697        | -               |
+| 1.3438 | 1450 | 0.0639        | -               |
+| 1.3902 | 1500 | 0.0729        | -               |
+| 1.4365 | 1550 | 0.0759        | -               |
+| 1.4829 | 1600 | 0.0786        | -               |
+| 1.5292 | 1650 | 0.0618        | -               |
+| 1.5755 | 1700 | 0.0722        | -               |
+| 1.6219 | 1750 | 0.0719        | -               |
+| 1.6682 | 1800 | 0.072         | -               |
+| 1.7146 | 1850 | 0.0654        | -               |
+| 1.7609 | 1900 | 0.0683        | -               |
+| 1.8072 | 1950 | 0.0654        | -               |
+| 1.8536 | 2000 | 0.0679        | -               |
+| 1.8999 | 2050 | 0.0643        | -               |
+| 1.9462 | 2100 | 0.0662        | -               |
+| 1.9926 | 2150 | 0.0642        | -               |
+| 2.0389 | 2200 | 0.0812        | -               |
+| 2.0853 | 2250 | 0.068         | -               |
+| 2.1316 | 2300 | 0.0583        | -               |
+| 2.1779 | 2350 | 0.0627        | -               |
+| 2.2243 | 2400 | 0.0654        | -               |
+| 2.2706 | 2450 | 0.0571        | -               |
+| 2.3170 | 2500 | 0.0623        | -               |
+| 2.3633 | 2550 | 0.0639        | -               |
+| 2.4096 | 2600 | 0.059         | -               |
+| 2.4560 | 2650 | 0.0637        | -               |
+| 2.5023 | 2700 | 0.0675        | -               |
+| 2.5487 | 2750 | 0.0696        | -               |
+| 2.5950 | 2800 | 0.0669        | -               |
+| 2.6413 | 2850 | 0.0633        | -               |
+| 2.6877 | 2900 | 0.0606        | -               |
+| 2.7340 | 2950 | 0.0609        | -               |
+| 2.7804 | 3000 | 0.054         | -               |
+| 2.8267 | 3050 | 0.0598        | -               |
+| 2.8730 | 3100 | 0.0597        | -               |
+| 2.9194 | 3150 | 0.0618        | -               |
+| 2.9657 | 3200 | 0.065         | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.1.1
+- Transformers: 4.39.0
+- PyTorch: 2.4.1+cu121
+- Datasets: 3.0.0
+- Tokenizers: 0.15.2
+
+## 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

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "cambridgeltl/SapBERT-from-PubMedBERT-fulltext",
+  "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": "4.39.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.39.0",
+    "pytorch": "2.4.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

@@ -0,0 +1,4 @@
+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d06b56258d86aece592a5a1c5d117c15ad68d262e2015db4221dd23355c30e08
+size 437951328

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38c0dad256433f167a10a8f5a7b32c5fcf3becb10746a8686a4f8eec99a6b7ae
+size 150849

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "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

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "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
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "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,
+  "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]"
+}