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
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: I wonder how times someone has wrecked trying to do the 'stare and drive'
+    move from 2 Fast 2 Furious
+- text: 'Plains All American Pipeline company may have spilled 40% more crude oil
+    than previously estimated #KSBYNews @lilitan http://t.co/PegibIqk2w'
+- text: 'ThisIsFaz: Anti Collision Rear- #technology #cool http://t.co/KEfxTjTAKB
+    Via Techesback #Tech'
+- text: Official kinesiology tape of IRONMANå¨ long-lasting durability effectiveness
+    on common injuries http://t.co/ejymkZPEEx http://t.co/0IYuntXDUv
+- text: Well as I was chaning an iPad screen it fucking exploded and glass went all
+    over the place. Looks like my job is going to need a new one.
+pipeline_tag: text-classification
+inference: true
+base_model: sentence-transformers/all-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/all-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8233459202101461
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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>'FOOTBALL IS BACK THIS WEEKEND ITS JUST SUNK IN ??????'</li><li>'Tried orange aftershock today. My life will never be the same'</li><li>"Attack on Titan game on PS Vita yay! Can't wait for 2016"</li></ul>                                                                                                                                                                                                       |
+| 1     | <ul><li>'@author_mike Amen today is the Day of Salvation. THX brother Mike for your great encouragement. - http://t.co/cybKsXHF7d Coming US Tsunami'</li><li>". @VELDFest announces refunds after Day two's extreme weather evacuation: http://t.co/PP05eTlK7t http://t.co/3Ol8MhhPMa"</li><li>'http://t.co/lMA39ZRWoY There is a way which seemeth right unto a man but the end thereof are the ways of death.'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8233   |
+
+## 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("pEpOo/catastrophy4")
+# Run inference
+preds = model("ThisIsFaz: Anti Collision Rear- #technology #cool http://t.co/KEfxTjTAKB Via Techesback #Tech")
+```
+
+<!--
+### 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   | 15.0486 | 30  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 836                   |
+| 1     | 686                   |
+
+### 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0003 | 1    | 0.4126        | -               |
+| 0.0131 | 50   | 0.2779        | -               |
+| 0.0263 | 100  | 0.2507        | -               |
+| 0.0394 | 150  | 0.2475        | -               |
+| 0.0526 | 200  | 0.1045        | -               |
+| 0.0657 | 250  | 0.2595        | -               |
+| 0.0788 | 300  | 0.1541        | -               |
+| 0.0920 | 350  | 0.1761        | -               |
+| 0.1051 | 400  | 0.0456        | -               |
+| 0.1183 | 450  | 0.1091        | -               |
+| 0.1314 | 500  | 0.1335        | -               |
+| 0.1445 | 550  | 0.0956        | -               |
+| 0.1577 | 600  | 0.0583        | -               |
+| 0.1708 | 650  | 0.0067        | -               |
+| 0.1840 | 700  | 0.0021        | -               |
+| 0.1971 | 750  | 0.0057        | -               |
+| 0.2102 | 800  | 0.065         | -               |
+| 0.2234 | 850  | 0.0224        | -               |
+| 0.2365 | 900  | 0.0008        | -               |
+| 0.2497 | 950  | 0.1282        | -               |
+| 0.2628 | 1000 | 0.1045        | -               |
+| 0.2760 | 1050 | 0.001         | -               |
+| 0.2891 | 1100 | 0.0005        | -               |
+| 0.3022 | 1150 | 0.0013        | -               |
+| 0.3154 | 1200 | 0.0007        | -               |
+| 0.3285 | 1250 | 0.0015        | -               |
+| 0.3417 | 1300 | 0.0007        | -               |
+| 0.3548 | 1350 | 0.0027        | -               |
+| 0.3679 | 1400 | 0.0006        | -               |
+| 0.3811 | 1450 | 0.0001        | -               |
+| 0.3942 | 1500 | 0.0009        | -               |
+| 0.4074 | 1550 | 0.0002        | -               |
+| 0.4205 | 1600 | 0.0004        | -               |
+| 0.4336 | 1650 | 0.0003        | -               |
+| 0.4468 | 1700 | 0.0013        | -               |
+| 0.4599 | 1750 | 0.0004        | -               |
+| 0.4731 | 1800 | 0.0007        | -               |
+| 0.4862 | 1850 | 0.0001        | -               |
+| 0.4993 | 1900 | 0.0001        | -               |
+| 0.5125 | 1950 | 0.0476        | -               |
+| 0.5256 | 2000 | 0.0561        | -               |
+| 0.5388 | 2050 | 0.0009        | -               |
+| 0.5519 | 2100 | 0.0381        | -               |
+| 0.5650 | 2150 | 0.017         | -               |
+| 0.5782 | 2200 | 0.033         | -               |
+| 0.5913 | 2250 | 0.0001        | -               |
+| 0.6045 | 2300 | 0.0077        | -               |
+| 0.6176 | 2350 | 0.0002        | -               |
+| 0.6307 | 2400 | 0.0003        | -               |
+| 0.6439 | 2450 | 0.0001        | -               |
+| 0.6570 | 2500 | 0.0155        | -               |
+| 0.6702 | 2550 | 0.0002        | -               |
+| 0.6833 | 2600 | 0.0001        | -               |
+| 0.6965 | 2650 | 0.031         | -               |
+| 0.7096 | 2700 | 0.0215        | -               |
+| 0.7227 | 2750 | 0.0002        | -               |
+| 0.7359 | 2800 | 0.0002        | -               |
+| 0.7490 | 2850 | 0.0001        | -               |
+| 0.7622 | 2900 | 0.0001        | -               |
+| 0.7753 | 2950 | 0.0001        | -               |
+| 0.7884 | 3000 | 0.0001        | -               |
+| 0.8016 | 3050 | 0.0001        | -               |
+| 0.8147 | 3100 | 0.0001        | -               |
+| 0.8279 | 3150 | 0.0001        | -               |
+| 0.8410 | 3200 | 0.0001        | -               |
+| 0.8541 | 3250 | 0.0001        | -               |
+| 0.8673 | 3300 | 0.0001        | -               |
+| 0.8804 | 3350 | 0.0001        | -               |
+| 0.8936 | 3400 | 0.0           | -               |
+| 0.9067 | 3450 | 0.0156        | -               |
+| 0.9198 | 3500 | 0.0           | -               |
+| 0.9330 | 3550 | 0.0           | -               |
+| 0.9461 | 3600 | 0.0001        | -               |
+| 0.9593 | 3650 | 0.0208        | -               |
+| 0.9724 | 3700 | 0.0           | -               |
+| 0.9855 | 3750 | 0.0001        | -               |
+| 0.9987 | 3800 | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.1
+- Sentence Transformers: 2.2.2
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.15.0
+- Tokenizers: 0.15.0
+
+## 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": "/root/.cache/torch/sentence_transformers/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": "4.35.2",
+  "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"
+  }
+}

config_setfit.json

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

model.safetensors

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+oid sha256:4239b4b70e203762d0a00e04c61ba821d74db58d6847a8e349385c0548953c20
+size 437967672

model_head.pkl

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+oid sha256:3ac71cdf67abdf186659f0e1b55416e4bbb5a754bef31d7c79845d9d594c0040
+size 6991

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": true,
+    "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": true,
+    "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
+    },
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+      "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]"
+}