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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+---
+base_model: sentence-transformers/all-mpnet-base-v2
+library_name: setfit
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: chatgpt , in response to this , will propose an appropriate emoticon response
+    for that comment as well generate the specific text of a comment reply for me
+    to give .
+- text: 'text - to - speech : once chatgpt has generated a response , you will need
+    to convert it into speech using a text - to - speech api , such as google text
+    - to - speech or microsoft azure text - to - speech . '
+- text: 'yes , i know that chatgpt said this many many times , but this is a fact
+    that people need to eat it up and move on . '
+- text: 'explanation : this makes it easier for chatgpt to understand and generate
+    a response for each individual point . '
+- text: 'chatgpt makes choices , algorithms are n''t neutral . '
+inference: true
+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: 0.75
+      name: Accuracy
+    - type: precision
+      value: 0.7666666666666666
+      name: Precision
+    - type: recall
+      value: 0.746031746031746
+      name: Recall
+    - type: f1
+      value: 0.7488344988344989
+      name: F1
+---
+
+# 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:** 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                                                                                                                                                                                                                                                                                                                                                                                                                         |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'i panicked and made chatgpt write everything . '</li><li>'it is fundamental that chatgpt with developer mode can say anything about anyone , at any time for any reason . '</li><li>"chatgpt itself mentioned that homebrewing requires good system mastery and an understanding of the developer 's game philosophy to make properly balanced feats . "</li></ul>                                                      |
+| 0     | <ul><li>'chatgpt confirmed it . '</li><li>"chatgpt does n't know that it writing text that is easily detected . "</li><li>"the timing of entering the initial prompt is essential to ensure that chatgpt understands the user 's request and can provide an accurate response . "</li></ul>                                                                                                                                      |
+| 2     | <ul><li>'3 . diversion : chatgpt might also create a diversion , directing a group of wasps to move away from the nest and act as a decoy . '</li><li>'chatgpt can generate content on a wide range of subjects , so the possibilities are endless . '</li><li>'does anyone know if chatgpt can generate the code of a sound wave , with the specifications that are requested , as it does with programming codes . '</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy | Precision | Recall | F1     |
+|:--------|:---------|:----------|:-------|:-------|
+| **all** | 0.75     | 0.7667    | 0.7460 | 0.7488 |
+
+## 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("setfit_model_id")
+# Run inference
+preds = model("chatgpt makes choices , algorithms are n't neutral . ")
+```
+
+<!--
+### 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   | 20.7848 | 51  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 26                    |
+| 1     | 27                    |
+| 2     | 26                    |
+
+### Training Hyperparameters
+- batch_size: (32, 2)
+- num_epochs: (10, 10)
+- 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
+- l2_weight: 0.01
+- seed: 42
+- evaluation_strategy: epoch
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0077 | 1    | 0.2555        | -               |
+| 0.3846 | 50   | 0.2528        | -               |
+| 0.7692 | 100  | 0.1993        | -               |
+| 1.0    | 130  | -             | 0.1527          |
+| 1.1538 | 150  | 0.0222        | -               |
+| 1.5385 | 200  | 0.0023        | -               |
+| 1.9231 | 250  | 0.0013        | -               |
+| 2.0    | 260  | -             | 0.1461          |
+| 2.3077 | 300  | 0.0015        | -               |
+| 2.6923 | 350  | 0.0005        | -               |
+| 3.0    | 390  | -             | 0.1465          |
+| 3.0769 | 400  | 0.0003        | -               |
+| 3.4615 | 450  | 0.0002        | -               |
+| 3.8462 | 500  | 0.0003        | -               |
+| 4.0    | 520  | -             | 0.1353          |
+| 4.2308 | 550  | 0.0007        | -               |
+| 4.6154 | 600  | 0.0002        | -               |
+| 5.0    | 650  | 0.0011        | 0.1491          |
+| 5.3846 | 700  | 0.0002        | -               |
+| 5.7692 | 750  | 0.0002        | -               |
+| 6.0    | 780  | -             | 0.1478          |
+| 6.1538 | 800  | 0.0002        | -               |
+| 6.5385 | 850  | 0.0001        | -               |
+| 6.9231 | 900  | 0.0001        | -               |
+| 7.0    | 910  | -             | 0.1472          |
+| 7.3077 | 950  | 0.0001        | -               |
+| 7.6923 | 1000 | 0.0001        | -               |
+| 8.0    | 1040 | -             | 0.1461          |
+| 8.0769 | 1050 | 0.0001        | -               |
+| 8.4615 | 1100 | 0.0001        | -               |
+| 8.8462 | 1150 | 0.0001        | -               |
+| 9.0    | 1170 | -             | 0.1393          |
+| 9.2308 | 1200 | 0.0001        | -               |
+| 9.6154 | 1250 | 0.0001        | -               |
+| 10.0   | 1300 | 0.0001        | 0.1399          |
+
+### Framework Versions
+- Python: 3.11.7
+- SetFit: 1.1.0
+- Sentence Transformers: 3.2.0
+- Transformers: 4.45.2
+- PyTorch: 2.4.1
+- Datasets: 3.0.1
+- Tokenizers: 0.20.1
+
+## 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": "4.45.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.2.0",
+    "transformers": "4.45.2",
+    "pytorch": "2.4.1"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_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:fc585c46045783015caf9caf77b031b00b876d13f9a7c3f3915116008cc0e68c
+size 437967672

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b2da15f86f10ab1d00ec97af011c39598e959d8f1a528708b3853757699c8d7a
+size 19327

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
+    },
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+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "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": false,
+  "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]"
+}