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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+---
+base_model: sentence-transformers/all-MiniLM-L6-v2
+library_name: setfit
+metrics:
+- f1
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget: []
+inference: true
+model-index:
+- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: f1
+      value: 0.5494505494505495
+      name: F1
+---
+
+# SetFit with sentence-transformers/all-MiniLM-L6-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 256 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)
+
+## Evaluation
+
+### Metrics
+| Label   | F1     |
+|:--------|:-------|
+| **all** | 0.5495 |
+
+## 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("Zlovoblachko/dimension3_setfit")
+# Run inference
+preds = model("I loved the spiderman movie!")
+```
+
+<!--
+### 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 Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (2.260895905036282e-05, 2.260895905036282e-05)
+- head_learning_rate: 0.01
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0004 | 1    | 0.3835        | -               |
+| 0.0177 | 50   | 0.3106        | -               |
+| 0.0353 | 100  | 0.3232        | -               |
+| 0.0530 | 150  | 0.319         | -               |
+| 0.0706 | 200  | 0.3146        | -               |
+| 0.0883 | 250  | 0.3194        | -               |
+| 0.1059 | 300  | 0.3166        | -               |
+| 0.1236 | 350  | 0.2941        | -               |
+| 0.1412 | 400  | 0.3289        | -               |
+| 0.1589 | 450  | 0.3108        | -               |
+| 0.1766 | 500  | 0.3099        | -               |
+| 0.1942 | 550  | 0.3072        | -               |
+| 0.2119 | 600  | 0.2994        | -               |
+| 0.2295 | 650  | 0.3062        | -               |
+| 0.2472 | 700  | 0.3046        | -               |
+| 0.2648 | 750  | 0.3086        | -               |
+| 0.2825 | 800  | 0.3039        | -               |
+| 0.3001 | 850  | 0.3096        | -               |
+| 0.3178 | 900  | 0.3134        | -               |
+| 0.3355 | 950  | 0.2965        | -               |
+| 0.3531 | 1000 | 0.3147        | -               |
+| 0.3708 | 1050 | 0.317         | -               |
+| 0.3884 | 1100 | 0.3123        | -               |
+| 0.4061 | 1150 | 0.3221        | -               |
+| 0.4237 | 1200 | 0.2971        | -               |
+| 0.4414 | 1250 | 0.2928        | -               |
+| 0.4590 | 1300 | 0.2977        | -               |
+| 0.4767 | 1350 | 0.3268        | -               |
+| 0.4944 | 1400 | 0.2785        | -               |
+| 0.5120 | 1450 | 0.3156        | -               |
+| 0.5297 | 1500 | 0.3148        | -               |
+| 0.5473 | 1550 | 0.2909        | -               |
+| 0.5650 | 1600 | 0.3225        | -               |
+| 0.5826 | 1650 | 0.3072        | -               |
+| 0.6003 | 1700 | 0.3099        | -               |
+| 0.6179 | 1750 | 0.311         | -               |
+| 0.6356 | 1800 | 0.3213        | -               |
+| 0.6532 | 1850 | 0.2937        | -               |
+| 0.6709 | 1900 | 0.3177        | -               |
+| 0.6886 | 1950 | 0.3088        | -               |
+| 0.7062 | 2000 | 0.3017        | -               |
+| 0.7239 | 2050 | 0.3076        | -               |
+| 0.7415 | 2100 | 0.3164        | -               |
+| 0.7592 | 2150 | 0.295         | -               |
+| 0.7768 | 2200 | 0.2957        | -               |
+| 0.7945 | 2250 | 0.3064        | -               |
+| 0.8121 | 2300 | 0.3146        | -               |
+| 0.8298 | 2350 | 0.3114        | -               |
+| 0.8475 | 2400 | 0.3151        | -               |
+| 0.8651 | 2450 | 0.3033        | -               |
+| 0.8828 | 2500 | 0.3039        | -               |
+| 0.9004 | 2550 | 0.3152        | -               |
+| 0.9181 | 2600 | 0.3185        | -               |
+| 0.9357 | 2650 | 0.2927        | -               |
+| 0.9534 | 2700 | 0.3174        | -               |
+| 0.9710 | 2750 | 0.3003        | -               |
+| 0.9887 | 2800 | 0.3157        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.2.1
+- Transformers: 4.44.2
+- PyTorch: 2.5.0+cu121
+- Datasets: 3.0.2
+- Tokenizers: 0.19.1
+
+## 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/all-MiniLM-L6-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": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.5.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "labels": [
+    "H",
+    "L"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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+oid sha256:1377e9af0ca0b016a9f2aa584d6fc71ab3ea6804fae21ef9fb1416e2944057ac
+size 90864192

model_head.pkl

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+oid sha256:0a7f0ddb7364f747e4cb3d42b328520098be4b5beb8454f65d4dd716d9cd7279
+size 2395

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": 256,
+  "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
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "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": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "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,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "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]"
+}