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:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Enable audio recorder app
+- text: Open video camera mode
+- text: Show recent chats
+- text: Switch to instant camera usage mode
+- text: Could you switch to video camera mode?
+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: accuracy
+      value: 1.0
+      name: Accuracy
+---
+
+# 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:** 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                                                                                                                          |
+|:-----------|:----------------------------------------------------------------------------------------------------------------------------------|
+| microphone | <ul><li>'Launch microphone app'</li><li>'Launch recording app'</li><li>'Access mic app'</li></ul>                                 |
+| history    | <ul><li>'View chat logs'</li><li>'Display conversation details'</li><li>'Show history'</li></ul>                                  |
+| camera     | <ul><li>'Switch to webcam mode please'</li><li>'Could you switch to video camera mode?'</li><li>'Open the photo webcam'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 1.0      |
+
+## 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("porxelek/word-classification")
+# Run inference
+preds = model("Show recent chats")
+```
+
+<!--
+### 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   | 4.1364 | 10  |
+
+| Label      | Training Sample Count |
+|:-----------|:----------------------|
+| camera     | 250                   |
+| history    | 150                   |
+| microphone | 150                   |
+
+### Training Hyperparameters
+- batch_size: (64, 64)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-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
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch   | Step     | Training Loss | Validation Loss |
+|:-------:|:--------:|:-------------:|:---------------:|
+| 0.0003  | 1        | 0.1209        | -               |
+| 0.0164  | 50       | 0.1449        | -               |
+| 0.0328  | 100      | 0.046         | -               |
+| 0.0492  | 150      | 0.0099        | -               |
+| 0.0656  | 200      | 0.0049        | -               |
+| 0.0820  | 250      | 0.0036        | -               |
+| 0.0985  | 300      | 0.0022        | -               |
+| 0.1149  | 350      | 0.0015        | -               |
+| 0.1313  | 400      | 0.0011        | -               |
+| 0.1477  | 450      | 0.001         | -               |
+| 0.1641  | 500      | 0.0009        | -               |
+| 0.1805  | 550      | 0.0009        | -               |
+| 0.1969  | 600      | 0.0009        | -               |
+| 0.2133  | 650      | 0.0008        | -               |
+| 0.2297  | 700      | 0.0007        | -               |
+| 0.2461  | 750      | 0.0006        | -               |
+| 0.2626  | 800      | 0.0006        | -               |
+| 0.2790  | 850      | 0.0006        | -               |
+| 0.2954  | 900      | 0.0006        | -               |
+| 0.3118  | 950      | 0.0005        | -               |
+| 0.3282  | 1000     | 0.0004        | -               |
+| 0.3446  | 1050     | 0.0005        | -               |
+| 0.3610  | 1100     | 0.0005        | -               |
+| 0.3774  | 1150     | 0.0004        | -               |
+| 0.3938  | 1200     | 0.0004        | -               |
+| 0.4102  | 1250     | 0.0004        | -               |
+| 0.4266  | 1300     | 0.0005        | -               |
+| 0.4431  | 1350     | 0.0004        | -               |
+| 0.4595  | 1400     | 0.0003        | -               |
+| 0.4759  | 1450     | 0.0003        | -               |
+| 0.4923  | 1500     | 0.0003        | -               |
+| 0.5087  | 1550     | 0.0003        | -               |
+| 0.5251  | 1600     | 0.0003        | -               |
+| 0.5415  | 1650     | 0.0003        | -               |
+| 0.5579  | 1700     | 0.0003        | -               |
+| 0.5743  | 1750     | 0.0003        | -               |
+| 0.5907  | 1800     | 0.0003        | -               |
+| 0.6072  | 1850     | 0.0002        | -               |
+| 0.6236  | 1900     | 0.0003        | -               |
+| 0.6400  | 1950     | 0.0002        | -               |
+| 0.6564  | 2000     | 0.0002        | -               |
+| 0.6728  | 2050     | 0.0002        | -               |
+| 0.6892  | 2100     | 0.0003        | -               |
+| 0.7056  | 2150     | 0.0002        | -               |
+| 0.7220  | 2200     | 0.0002        | -               |
+| 0.7384  | 2250     | 0.0002        | -               |
+| 0.7548  | 2300     | 0.0002        | -               |
+| 0.7713  | 2350     | 0.0002        | -               |
+| 0.7877  | 2400     | 0.0002        | -               |
+| 0.8041  | 2450     | 0.0002        | -               |
+| 0.8205  | 2500     | 0.0002        | -               |
+| 0.8369  | 2550     | 0.0002        | -               |
+| 0.8533  | 2600     | 0.0002        | -               |
+| 0.8697  | 2650     | 0.0002        | -               |
+| 0.8861  | 2700     | 0.0002        | -               |
+| 0.9025  | 2750     | 0.0002        | -               |
+| 0.9189  | 2800     | 0.0002        | -               |
+| 0.9353  | 2850     | 0.0002        | -               |
+| 0.9518  | 2900     | 0.0002        | -               |
+| 0.9682  | 2950     | 0.0002        | -               |
+| 0.9846  | 3000     | 0.0002        | -               |
+| **1.0** | **3047** | **-**         | **0.0**         |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- Transformers: 4.39.0
+- PyTorch: 2.3.1+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": "checkpoints/step_3047",
+  "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": "4.39.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.39.0",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": [
+    "camera",
+    "history",
+    "microphone"
+  ]
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ef716de1af815df89a03cc9ca0cea5960d00d1c71d2f2dda30b3924327735c92
+size 90864192

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c1d7a111eede97b42219763ff997d3ade8bc9b62420dcfb4a307239b58016e6f
+size 10207

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "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]"
+}