Add SetFit model

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
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+datasets:
+- konsman/setfit-messages-updated-influence-level
+metrics:
+- accuracy
+widget:
+- text: The influence level of Staying hydrated is especially important for older
+    adults to prevent dehydration.
+- text: The influence level of Regularly updating emergency contact information is
+    important for the elderly.
+- text: 'The influence level of Early detection saves lives. Support breast cancer
+    awareness month.  Wear pink, spread awareness. Stand with us this breast cancer
+    awareness month. '
+- text: 'The influence level of Mental Health Day is approaching. Join our online
+    discussion on well-being.  Prioritize mental health. Participate in our online
+    discussion this Mental Health Day. '
+- text: The influence level of Regular kidney function tests are important for those
+    with high blood pressure.
+pipeline_tag: text-classification
+inference: true
+base_model: sentence-transformers/all-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/all-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: konsman/setfit-messages-updated-influence-level
+      type: konsman/setfit-messages-updated-influence-level
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.47368421052631576
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/all-mpnet-base-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [konsman/setfit-messages-updated-influence-level](https://huggingface.co/datasets/konsman/setfit-messages-updated-influence-level) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 384 tokens
+- **Number of Classes:** 4 classes
+- **Training Dataset:** [konsman/setfit-messages-updated-influence-level](https://huggingface.co/datasets/konsman/setfit-messages-updated-influence-level)
+<!-- - **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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'The influence level of Understanding the effects of aging on the body is key for caregivers.'</li><li>'The influence level of Regular check-ups are key to maintaining good health.'</li><li>'The influence level of Balanced nutrition is key for maintaining health in old age.'</li></ul>                                                                                                                                                                                                                                 |
+| 1     | <ul><li>"The influence level of Time for your 3pm medication! Please take as directed.  Friendly reminder: It's time for your 3pm medication. Ensure to take it as prescribed."</li><li>'The influence level of Regular bladder function tests are important for elderly individuals.'</li><li>'The influence level of How was your telehealth session? Share your feedback.  Help us improve. Provide feedback on your recent telehealth appointment. '</li></ul>                                                                    |
+| 2     | <ul><li>"The influence level of A support group meeting is scheduled for tomorrow at 5pm. It's a great opportunity to share and learn.  Connect with others in our support group meeting tomorrow. See you at 5pm!"</li><li>'The influence level of Safety first! Please update your emergency contact details in our system.  Ensure swift help when needed. Update your emergency contacts in our app. '</li><li>'The influence level of Regularly discussing health concerns with doctors is important for the elderly.'</li></ul> |
+| 3     | <ul><li>'The influence level of Understanding the role of dietary supplements in elderly health is important.'</li><li>'The influence level of Proper medication management is essential for effective treatment.'</li><li>"The influence level of Your child's health is paramount. Reminder for the pediatrician appointment tomorrow.  Ensure the best for your child. Don't miss the pediatrician appointment set for tomorrow. "</li></ul>                                                                                       |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.4737   |
+
+## 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("konsman/setfit-messages-label-v2")
+# Run inference
+preds = model("The influence level of Regularly updating emergency contact information is important for the elderly.")
+```
+
+<!--
+### 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   | 12  | 20.8438 | 36  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 8                     |
+| 1     | 8                     |
+| 2     | 8                     |
+| 3     | 8                     |
+
+### Training Hyperparameters
+- batch_size: (8, 8)
+- num_epochs: (4, 4)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 40
+- body_learning_rate: (2.2041595048800003e-05, 2.2041595048800003e-05)
+- head_learning_rate: 2.2041595048800003e-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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0031 | 1    | 0.1587        | -               |
+| 0.1562 | 50   | 0.116         | -               |
+| 0.3125 | 100  | 0.0918        | -               |
+| 0.4688 | 150  | 0.0042        | -               |
+| 0.625  | 200  | 0.0005        | -               |
+| 0.7812 | 250  | 0.0012        | -               |
+| 0.9375 | 300  | 0.0005        | -               |
+| 1.0938 | 350  | 0.0005        | -               |
+| 1.25   | 400  | 0.0003        | -               |
+| 1.4062 | 450  | 0.0002        | -               |
+| 1.5625 | 500  | 0.0002        | -               |
+| 1.7188 | 550  | 0.0001        | -               |
+| 1.875  | 600  | 0.0001        | -               |
+| 2.0312 | 650  | 0.0002        | -               |
+| 2.1875 | 700  | 0.0001        | -               |
+| 2.3438 | 750  | 0.0001        | -               |
+| 2.5    | 800  | 0.0001        | -               |
+| 2.6562 | 850  | 0.0001        | -               |
+| 2.8125 | 900  | 0.0001        | -               |
+| 2.9688 | 950  | 0.0001        | -               |
+| 3.125  | 1000 | 0.0002        | -               |
+| 3.2812 | 1050 | 0.0001        | -               |
+| 3.4375 | 1100 | 0.0001        | -               |
+| 3.5938 | 1150 | 0.0001        | -               |
+| 3.75   | 1200 | 0.0001        | -               |
+| 3.9062 | 1250 | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.2
+- Sentence Transformers: 2.2.2
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.16.1
+- Tokenizers: 0.15.0
+
+## 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": "/root/.cache/torch/sentence_transformers/sentence-transformers_all-mpnet-base-v2/",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  }
+}

config_setfit.json

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

model.safetensors

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+oid sha256:9b3aa7622a726a7efecc525d6aeb25fed8761aba3bd7898425392e76295e5a83
+size 437967672

model_head.pkl

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+size 25463

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"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 384,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
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+    "lstrip": false,
+    "normalized": true,
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "mask_token": {
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+    "lstrip": true,
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+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "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": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
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+      "single_word": false,
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+    "30526": {
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}