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
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+base_model: sentence-transformers/all-mpnet-base-v2
+datasets:
+- bhaskars113/toyota-paint-attributes
+metrics:
+- accuracy
+widget:
+- text: Hey guys, I'm buying a 2004 Mach 1 Mustang and I'm super excited! It's in
+    great condition and has only had one owner. Only thing is the grill mustang ornament
+    was stolen years ago he said and he never bothered to replace it. After searching
+    online I cannot find anything that's at least a reliable source. I am in Canada
+    by the way. If anyone knows how to search one down I would be very appreciative!
+    Thanks!
+- text: Mine is actually gold! I think the official paint name is harvest gold. It's
+    nice but I'd rather something like the two-tone paints of the 2nd gen. The dull
+    metallic gold reminds me of boring grey old corollas lol
+- text: Arrgh. Click to expand... Welcome to owning a Jeep/Dodge product. in 150,000km
+    of ownership of our Jeep, we have replaced everything in the suspension 2 times,
+    throttle body, 3 sets of plugs, various electrical things, stereo pooped the bed,
+    I could go on and on. The most reliable dodge/jeep product I owned was my 2011
+    Wrangler Once I removed all the dumb design features jeep put there, like freaking
+    plastic in the ball joints. Move to another brand and be MUCH happier. We have
+    179k on our Ford F150 5.0 and all that's been replaced is one set of plugs and
+    one ball joint.
+- text: The car is from Utah and garage kept, so the paint is still in very good condition
+- text: I've seen wonders done by a good paintless dent repair professional. The right
+    person with the right tools could make this look brand new, or at least better
+    than slightly mismatched paint.
+pipeline_tag: text-classification
+inference: false
+---
+
+# SetFit with sentence-transformers/all-mpnet-base-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [bhaskars113/toyota-paint-attributes](https://huggingface.co/datasets/bhaskars113/toyota-paint-attributes) 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 OneVsRestClassifier 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 OneVsRestClassifier instance
+- **Maximum Sequence Length:** 384 tokens
+<!-- - **Number of Classes:** Unknown -->
+- **Training Dataset:** [bhaskars113/toyota-paint-attributes](https://huggingface.co/datasets/bhaskars113/toyota-paint-attributes)
+<!-- - **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)
+
+## 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("bhaskars113/toyota-paint-attribute-1.1")
+# Run inference
+preds = model("The car is from Utah and garage kept, so the paint is still in very good condition")
+```
+
+<!--
+### 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   | 5   | 33.8098 | 155 |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0004 | 1    | 0.1664        | -               |
+| 0.0196 | 50   | 0.2377        | -               |
+| 0.0392 | 100  | 0.1178        | -               |
+| 0.0588 | 150  | 0.0577        | -               |
+| 0.0784 | 200  | 0.0163        | -               |
+| 0.0980 | 250  | 0.0265        | -               |
+| 0.1176 | 300  | 0.0867        | -               |
+| 0.1373 | 350  | 0.0181        | -               |
+| 0.1569 | 400  | 0.0153        | -               |
+| 0.1765 | 450  | 0.0411        | -               |
+| 0.1961 | 500  | 0.0308        | -               |
+| 0.2157 | 550  | 0.0258        | -               |
+| 0.2353 | 600  | 0.0062        | -               |
+| 0.2549 | 650  | 0.0036        | -               |
+| 0.2745 | 700  | 0.0087        | -               |
+| 0.2941 | 750  | 0.0025        | -               |
+| 0.3137 | 800  | 0.004         | -               |
+| 0.3333 | 850  | 0.0025        | -               |
+| 0.3529 | 900  | 0.0044        | -               |
+| 0.3725 | 950  | 0.0031        | -               |
+| 0.3922 | 1000 | 0.0018        | -               |
+| 0.4118 | 1050 | 0.0046        | -               |
+| 0.4314 | 1100 | 0.0013        | -               |
+| 0.4510 | 1150 | 0.0014        | -               |
+| 0.4706 | 1200 | 0.002         | -               |
+| 0.4902 | 1250 | 0.0015        | -               |
+| 0.5098 | 1300 | 0.0039        | -               |
+| 0.5294 | 1350 | 0.0019        | -               |
+| 0.5490 | 1400 | 0.0011        | -               |
+| 0.5686 | 1450 | 0.0008        | -               |
+| 0.5882 | 1500 | 0.0015        | -               |
+| 0.6078 | 1550 | 0.0012        | -               |
+| 0.6275 | 1600 | 0.0011        | -               |
+| 0.6471 | 1650 | 0.0008        | -               |
+| 0.6667 | 1700 | 0.0016        | -               |
+| 0.6863 | 1750 | 0.0009        | -               |
+| 0.7059 | 1800 | 0.0008        | -               |
+| 0.7255 | 1850 | 0.0008        | -               |
+| 0.7451 | 1900 | 0.0008        | -               |
+| 0.7647 | 1950 | 0.0011        | -               |
+| 0.7843 | 2000 | 0.0008        | -               |
+| 0.8039 | 2050 | 0.001         | -               |
+| 0.8235 | 2100 | 0.001         | -               |
+| 0.8431 | 2150 | 0.0009        | -               |
+| 0.8627 | 2200 | 0.0067        | -               |
+| 0.8824 | 2250 | 0.0008        | -               |
+| 0.9020 | 2300 | 0.0009        | -               |
+| 0.9216 | 2350 | 0.0009        | -               |
+| 0.9412 | 2400 | 0.0007        | -               |
+| 0.9608 | 2450 | 0.0006        | -               |
+| 0.9804 | 2500 | 0.0007        | -               |
+| 1.0    | 2550 | 0.0006        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.40.2
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.19.1
+- 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-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": "4.40.2",
+  "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"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0836116e7ac74c7a52745ed956ff30df409a79c3f9c4172bcf72fb1f1ef95368
+size 437967672

model_head.pkl

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+oid sha256:0159592c96d1cf914aa1c9b07d4d3492feb20f3a155cd1d7aaec3622362e48c6
+size 195396

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": 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
+    },
+    "2": {
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+      "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": 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]"
+}