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,
+  "include_prompt": true
+}

README.md

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+---
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Now that the baffling, elongated, hyperreal coronation has occurred—no, not
+    that one—and Liz Truss has become Prime Minister, a degree of intervention and
+    action on energy bills has emerged, ahead of the looming socioeconomic catastrophe
+    facing the country this winter.
+- text: But it needs to go much further.
+- text: What could possibly go wrong?
+- text: If you are White you might feel bad about hurting others or you might feel
+    afraid to lose this privilege….Overcoming White privilege is a job that must start
+    with the White community….
+- text: '[JF: Obviously, immigration wasn’t stopped: the current population of the
+    United States is 329.5 million—it passed 300 million in 2006.'
+inference: true
+---
+
+# SetFit
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A SVC 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:** [Unknown](https://huggingface.co/unknown) -->
+- **Classification head:** a SVC instance
+- **Maximum Sequence Length:** 384 tokens
+- **Number of Classes:** 2 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>'ESG funds often charge many times more for investment funds that are nearly indistinguishable from those without the ESG title.'</li><li>'They are California, Florida, Illinois, Nebraska, New York, and Wyoming.'</li><li>'And so it goes.'</li></ul>                                                                                                                                                                                                                          |
+| 1     | <ul><li>'Republicans attempted to pass a resolution that would have enabled Congress to force workers to accept a deal, which was fortunately blocked by (who else) Senator Bernie Sanders.'</li><li>'No government ever surrenders power, even its emergency powers—not really.'</li><li>'No citizen in a democratic society should want executives from $10trn financial institutions to play a larger role than they already do in defining and implementing social values.'</li></ul> |
+
+## 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("SOUMYADEEPSAR/Setfit_random_sample_svm_head")
+# Run inference
+preds = model("What could possibly go wrong?")
+```
+
+<!--
+### 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   | 3   | 23.4159 | 68  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 136                   |
+| 1     | 78                    |
+
+### Training Hyperparameters
+- batch_size: (8, 8)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0003 | 1    | 0.3597        | -               |
+| 0.0161 | 50   | 0.2693        | -               |
+| 0.0323 | 100  | 0.2501        | -               |
+| 0.0484 | 150  | 0.2691        | -               |
+| 0.0645 | 200  | 0.063         | -               |
+| 0.0806 | 250  | 0.0179        | -               |
+| 0.0968 | 300  | 0.0044        | -               |
+| 0.1129 | 350  | 0.0003        | -               |
+| 0.1290 | 400  | 0.0005        | -               |
+| 0.1452 | 450  | 0.0002        | -               |
+| 0.1613 | 500  | 0.0003        | -               |
+| 0.1774 | 550  | 0.0001        | -               |
+| 0.1935 | 600  | 0.0001        | -               |
+| 0.2097 | 650  | 0.0001        | -               |
+| 0.2258 | 700  | 0.0001        | -               |
+| 0.2419 | 750  | 0.0001        | -               |
+| 0.2581 | 800  | 0.0           | -               |
+| 0.2742 | 850  | 0.0001        | -               |
+| 0.2903 | 900  | 0.0002        | -               |
+| 0.3065 | 950  | 0.0           | -               |
+| 0.3226 | 1000 | 0.0           | -               |
+| 0.3387 | 1050 | 0.0002        | -               |
+| 0.3548 | 1100 | 0.0           | -               |
+| 0.3710 | 1150 | 0.0001        | -               |
+| 0.3871 | 1200 | 0.0001        | -               |
+| 0.4032 | 1250 | 0.0           | -               |
+| 0.4194 | 1300 | 0.0           | -               |
+| 0.4355 | 1350 | 0.0           | -               |
+| 0.4516 | 1400 | 0.0001        | -               |
+| 0.4677 | 1450 | 0.0           | -               |
+| 0.4839 | 1500 | 0.0           | -               |
+| 0.5    | 1550 | 0.0001        | -               |
+| 0.5161 | 1600 | 0.0001        | -               |
+| 0.5323 | 1650 | 0.0           | -               |
+| 0.5484 | 1700 | 0.0           | -               |
+| 0.5645 | 1750 | 0.0           | -               |
+| 0.5806 | 1800 | 0.0           | -               |
+| 0.5968 | 1850 | 0.0           | -               |
+| 0.6129 | 1900 | 0.0           | -               |
+| 0.6290 | 1950 | 0.0001        | -               |
+| 0.6452 | 2000 | 0.0           | -               |
+| 0.6613 | 2050 | 0.0           | -               |
+| 0.6774 | 2100 | 0.0           | -               |
+| 0.6935 | 2150 | 0.0001        | -               |
+| 0.7097 | 2200 | 0.0           | -               |
+| 0.7258 | 2250 | 0.0           | -               |
+| 0.7419 | 2300 | 0.0001        | -               |
+| 0.7581 | 2350 | 0.0001        | -               |
+| 0.7742 | 2400 | 0.0001        | -               |
+| 0.7903 | 2450 | 0.0           | -               |
+| 0.8065 | 2500 | 0.0           | -               |
+| 0.8226 | 2550 | 0.0           | -               |
+| 0.8387 | 2600 | 0.0           | -               |
+| 0.8548 | 2650 | 0.0001        | -               |
+| 0.8710 | 2700 | 0.0001        | -               |
+| 0.8871 | 2750 | 0.0           | -               |
+| 0.9032 | 2800 | 0.0           | -               |
+| 0.9194 | 2850 | 0.0           | -               |
+| 0.9355 | 2900 | 0.0001        | -               |
+| 0.9516 | 2950 | 0.0           | -               |
+| 0.9677 | 3000 | 0.0001        | -               |
+| 0.9839 | 3050 | 0.0           | -               |
+| 1.0    | 3100 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- Transformers: 4.39.0
+- PyTorch: 2.3.0+cu121
+- Datasets: 2.20.0
+- 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/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": "4.39.0",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.39.0",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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

model.safetensors

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+oid sha256:87f08cf9353b5d8747a06789992fd2a93bab52c5793e8d9436e506a7d9002d16
+size 437967672

model_head.pkl

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+oid sha256:4746947ce49169108e45a1a8b4948d891eb7ccead7c3cbc4570eca040612959c
+size 75611

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": {
+    "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": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "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_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 384,
+  "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]"
+}