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,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: we get some truly unique character studies and a cross-section of americana
+    that hollywood could n't possibly fictionalize and be believed .
+- text: the movie is one of the best examples of artful large format filmmaking you
+    are likely to see anytime soon .
+- text: my response to the film is best described as lukewarm .
+- text: the movie 's ripe , enrapturing beauty will tempt those willing to probe its
+    inscrutable mysteries .
+- text: fear dot com is so rambling and disconnected it never builds any suspense
+    .
+pipeline_tag: text-classification
+inference: true
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.5380090497737556
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 5 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>"it 's not a motion picture ; it 's an utterly static picture ."</li><li>"frankly , it 's kind of insulting , both to men and women ."</li><li>'under-rehearsed and lifeless'</li></ul>                                                                                                                                                                                                                                                                               |
+| 2     | <ul><li>"recoing 's fantastic performance does n't exactly reveal what makes vincent tick , but perhaps any definitive explanation for it would have felt like a cheat ."</li><li>"do n't expect any subtlety from this latest entry in the increasingly threadbare gross-out comedy cycle ."</li><li>"merry friggin ' christmas !"</li></ul>                                                                                                                                 |
+| 3     | <ul><li>"so purely enjoyable that you might not even notice it 's a fairly straightforward remake of hollywood comedies such as father of the bride ."</li><li>"what saves this deeply affecting film from being merely a collection of wrenching cases is corcuera 's attention to detail ."</li><li>'for once , a movie does not proclaim the truth about two love-struck somebodies , but permits them time and space to convince us of that all on their own .'</li></ul> |
+| 1     | <ul><li>"the fact that it is n't very good is almost beside the point ."</li><li>'what starts off as a satisfying kids flck becomes increasingly implausible as it races through contrived plot points .'</li><li>'the film is ultimately about as inspiring as a hallmark card .'</li></ul>                                                                                                                                                                                  |
+| 4     | <ul><li>'cool gadgets and creatures keep this fresh .'</li><li>'morton deserves an oscar nomination .'</li><li>'a brutal and funny work .'</li></ul>                                                                                                                                                                                                                                                                                                                          |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.5380   |
+
+## 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("vidhi0206/setfit-paraphrase-mpnet-sst5_v2")
+# Run inference
+preds = model("my response to the film is best described as lukewarm .")
+```
+
+<!--
+### 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   | 18.8062 | 52  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 64                    |
+| 1     | 64                    |
+| 2     | 64                    |
+| 3     | 64                    |
+| 4     | 64                    |
+
+### Training Hyperparameters
+- batch_size: (8, 8)
+- 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
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0006 | 1    | 0.2259        | -               |
+| 0.0312 | 50   | 0.2373        | -               |
+| 0.0625 | 100  | 0.1726        | -               |
+| 0.0938 | 150  | 0.1607        | -               |
+| 0.125  | 200  | 0.1869        | -               |
+| 0.1562 | 250  | 0.1863        | -               |
+| 0.1875 | 300  | 0.224         | -               |
+| 0.2188 | 350  | 0.1625        | -               |
+| 0.25   | 400  | 0.1284        | -               |
+| 0.2812 | 450  | 0.1357        | -               |
+| 0.3125 | 500  | 0.2193        | -               |
+| 0.3438 | 550  | 0.1434        | -               |
+| 0.375  | 600  | 0.0524        | -               |
+| 0.4062 | 650  | 0.0558        | -               |
+| 0.4375 | 700  | 0.072         | -               |
+| 0.4688 | 750  | 0.0312        | -               |
+| 0.5    | 800  | 0.0732        | -               |
+| 0.5312 | 850  | 0.0117        | -               |
+| 0.5625 | 900  | 0.0311        | -               |
+| 0.5938 | 950  | 0.0228        | -               |
+| 0.625  | 1000 | 0.0026        | -               |
+| 0.6562 | 1050 | 0.0196        | -               |
+| 0.6875 | 1100 | 0.0017        | -               |
+| 0.7188 | 1150 | 0.0067        | -               |
+| 0.75   | 1200 | 0.0029        | -               |
+| 0.7812 | 1250 | 0.0041        | -               |
+| 0.8125 | 1300 | 0.0006        | -               |
+| 0.8438 | 1350 | 0.0022        | -               |
+| 0.875  | 1400 | 0.0006        | -               |
+| 0.9062 | 1450 | 0.0007        | -               |
+| 0.9375 | 1500 | 0.001         | -               |
+| 0.9688 | 1550 | 0.0009        | -               |
+| 1.0    | 1600 | 0.0013        | -               |
+
+### Framework Versions
+- Python: 3.8.10
+- SetFit: 1.0.3
+- Sentence Transformers: 2.3.1
+- Transformers: 4.37.2
+- PyTorch: 2.2.0+cu121
+- Datasets: 2.17.0
+- Tokenizers: 0.15.1
+
+## 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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+{
+  "_name_or_path": "sentence-transformers/paraphrase-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": "4.37.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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

config_setfit.json

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

model.safetensors

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+size 437967672

model_head.pkl

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+oid sha256:f4e60b138da2ff026e163458431e4b45b5f621e3d8a24a4f50f584a39383e266
+size 31631

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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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "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
+    },
+    "1": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}