Add SetFit model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "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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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: He is Male, his heart rate is 148, he walks 10000 steps daily, and is Normal.
+    He slept at 1 hrs. Yesterday, he slept from 2.0hrs to 3.0 hrs, with a duration
+    of 90.0 minutes and 0 interruptions. The day before yesterday, he slept from 22.0
+    hrs to 6.0 hrs, with a duration of 485.0 minutes and 0 interruptions.
+- text: She is Female, her heart rate is 68, she walks 11000 steps daily and is Normal.
+    She slept at 1 hrs. Yesterday, she slept from 1.0 hrs to 9.0 hrs, with a duration
+    of 495.0 minutes and 0 interruptions. The day before yesterday, she slept from
+    1.0 hrs to 10.0 hrs, with a duration of 540.0 minutes and 0 interruptions.
+- text: He is Male, his heart rate is 70, he walks 8500 steps daily, and is Normal.
+    He slept at 23 hrs. Yesterday, he slept from 23.0hrs to 8.0 hrs, with a duration
+    of 350.0 minutes and 3 interruptions. The day before yesterday, he slept from
+    22.0 hrs to 6.0 hrs, with a duration of 390.0 minutes and 1 interruptions.
+- text: He is Male, his heart rate is 93, he walks 9800 steps daily, and is Normal.
+    He slept at 0 hrs. Yesterday, he slept from 23.0hrs to 7.0 hrs, with a duration
+    of 460.0 minutes and 0 interruptions. The day before yesterday, he slept from
+    23.0 hrs to 7.0 hrs, with a duration of 425.0 minutes and 1 interruptions.
+- text: He is Male, his heart rate is 75, he walks 11000 steps daily, and is Normal.
+    He slept at 2 hrs. Yesterday, he slept from 3.0hrs to 7.0 hrs, with a duration
+    of 400.0 minutes and 2 interruptions. The day before yesterday, he slept from
+    1.0 hrs to 8.0 hrs, with a duration of 450.0 minutes and 3 interruptions.
+pipeline_tag: text-classification
+inference: true
+base_model: sentence-transformers/paraphrase-MiniLM-L3-v2
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8666666666666667
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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:** [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 128 tokens
+- **Number of Classes:** 3 classes
+<!-- - **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)
+
+### Model Labels
+| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'He is Male, his heart rate is 64, he walks 10000 steps daily, and is Normal. He slept at 11 hrs. Yesterday, he slept from 22.0hrs to 11.0 hrs, with a duration of 765.0 minutes and 2 interruptions. The day before yesterday, he slept from 23.0 hrs to 8.0 hrs, with a duration of 527.0 minutes and 4 interruptions.'</li><li>'She is Female, her heart rate is 89, she walks 3873 steps daily and is Overweight. She slept at 10 hrs. Yesterday, she slept from 4.0 hrs to 6.0 hrs, with a duration of 120.0 minutes and 1 interruptions. The day before yesterday, she slept from 4.0 hrs to 9.0 hrs, with a duration of 300.0 minutes and 2 interruptions.'</li><li>'She is Female, her heart rate is 68, she walks 11000 steps daily and is Normal. She slept at 10 hrs. Yesterday, she slept from 1.0 hrs to 9.0 hrs, with a duration of 495.0 minutes and 0 interruptions. The day before yesterday, she slept from 1.0 hrs to 10.0 hrs, with a duration of 540.0 minutes and 1 interruptions.'</li></ul> |
+| 2     | <ul><li>'She is Female, her heart rate is 66, she walks 2413 steps daily and is Underweight. She slept at 8 hrs. Yesterday, she slept from 23.0 hrs to 7.0 hrs, with a duration of 472.0 minutes and 5 interruptions. The day before yesterday, she slept from 23.0 hrs to 5.0 hrs, with a duration of 344.0 minutes and 6 interruptions.'</li><li>'He is Male, his heart rate is 95, he walks 9000 steps daily, and is Normal. He slept at 10 hrs. Yesterday, he slept from 4.0hrs to 9.0 hrs, with a duration of 323.0 minutes and 5 interruptions. The day before yesterday, he slept from 2.0 hrs to 10.0 hrs, with a duration of 501.0 minutes and 6 interruptions.'</li></ul>                                                                                                                                                                                                                                                                                                                                         |
+| 0     | <ul><li>'She is Female, her heart rate is 100, she walks 8000 steps daily and is Normal. She slept at 7 hrs. Yesterday, she slept from 2.0 hrs to 7.0 hrs, with a duration of 323.0 minutes and 0 interruptions. The day before yesterday, she slept from 0.0 hrs to 6.0 hrs, with a duration of 395.0 minutes and 2 interruptions.'</li><li>'He is Male, his heart rate is 93, he walks 9800 steps daily, and is Normal. He slept at 9 hrs. Yesterday, he slept from 23.0hrs to 7.0 hrs, with a duration of 460.0 minutes and 0 interruptions. The day before yesterday, he slept from 23.0 hrs to 7.0 hrs, with a duration of 425.0 minutes and 1 interruptions.'</li></ul>                                                                                                                                                                                                                                                                                                                                               |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8667   |
+
+## 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("reecursion/few-shot-stress-detection-miniLM")
+# Run inference
+preds = model("He is Male, his heart rate is 75, he walks 11000 steps daily, and is Normal. He slept at 2 hrs. Yesterday, he slept from 3.0hrs to 7.0 hrs, with a duration of 400.0 minutes and 2 interruptions. The day before yesterday, he slept from 1.0 hrs to 8.0 hrs, with a duration of 450.0 minutes and 3 interruptions.")
+```
+
+<!--
+### 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   | 59  | 59.5   | 60  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 2                     |
+| 1     | 6                     |
+| 2     | 2                     |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- 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
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0526 | 1    | 0.3562        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.6.1
+- Transformers: 4.38.2
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.18.0
+- 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

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+{
+  "_name_or_path": "sentence-transformers/paraphrase-MiniLM-L3-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 3,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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

model.safetensors

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+oid sha256:2cb8a028e05f5375d86498c46747c328b1b31ebb6920224ee15517b228f9a901
+size 69565312

model_head.pkl

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+size 10111

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": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
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+      "lstrip": false,
+      "normalized": false,
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+      "special": true
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}