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 recently purchased the Reevati Gold Pearl Necklace and upon receiving it,
+    I noticed that the pearls are not properly aligned and some seem to be of different
+    sizes. This is not what I expected based on the images on your site.
+- text: I recently ordered the Once in a Blue Moon Statement Ring but haven't received
+    any shipping updates yet. Can you provide me with the current status of my order?
+- text: I recently bought the Golden Love Affair Pendant, but it seems to have tarnished
+    very quickly. I'm not satisfied with the quality. What can you do about this?
+- text: I recently purchased the Three Crystal Proposal Ring, but I'm disappointed
+    to find that one of the crystals is loose. Can you assist me with this issue?
+- text: I recently purchased the Bloomingdale Pendant, but I've noticed that the quality
+    does not meet the standards promised on the website. The pendant looks tarnished
+    and is different from the images shown.
+pipeline_tag: text-classification
+inference: true
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8024691358024691
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 4 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                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+|:------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| product faq             | <ul><li>'What are the different sizes available for the Love is in the Air Proposal Ring, and do they come at different price points?'</li><li>'What is the material of the Open Pear Cut Ring and are there different sizes available?'</li><li>'What is the material used for making the Golden Spin Hoop Earring, and does it come with any kind of warranty or guarantee?'</li></ul>                                                                            |
+| product discoveribility | <ul><li>'What are the latest choker styles available for a wedding occasion?'</li><li>"I'm interested in sustainable jewelry; do you have any eco-friendly necklaces?"</li><li>'Could you recommend some necklaces with a vintage vibe to them?'</li></ul>                                                                                                                                                                                                          |
+| order tracking          | <ul><li>'I recently purchased the Seher Pearl Choker Set and I would like to know the current status of my order delivery.'</li><li>"I placed an order for the Tiara Silver Ring, but I haven't received any shipping updates yet. Can you provide me with the current status of my order?"</li><li>'I recently ordered the Toes Of Love Pendant but have not received any shipping confirmation. Could you please provide me with the tracking details?'</li></ul> |
+| product policy          | <ul><li>'Are there any restocking fees for bracelet returns?'</li><li>"Can I exchange a ring if it doesn't fit properly?"</li><li>'Are there any care instructions included with the purchase of a ring?'</li></ul>                                                                                                                                                                                                                                                 |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8025   |
+
+## 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("I recently purchased the Three Crystal Proposal Ring, but I'm disappointed to find that one of the crystals is loose. Can you assist me with this issue?")
+```
+
+<!--
+### 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   | 6   | 16.4474 | 30  |
+
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| negative | 0                     |
+| positive | 0                     |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (4, 4)
+- 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 |
+|:-------:|:-------:|:-------------:|:---------------:|
+| 0.0016  | 1       | 0.1464        | -               |
+| 0.0822  | 50      | 0.0907        | -               |
+| 0.1645  | 100     | 0.0059        | -               |
+| 0.2467  | 150     | 0.0013        | -               |
+| 0.3289  | 200     | 0.0009        | -               |
+| 0.4112  | 250     | 0.0007        | -               |
+| 0.4934  | 300     | 0.0004        | -               |
+| 0.5757  | 350     | 0.0003        | -               |
+| 0.6579  | 400     | 0.0001        | -               |
+| 0.7401  | 450     | 0.0002        | -               |
+| 0.8224  | 500     | 0.0002        | -               |
+| 0.9046  | 550     | 0.0002        | -               |
+| 0.9868  | 600     | 0.0001        | -               |
+| **1.0** | **608** | **-**         | **0.2272**      |
+| 1.0691  | 650     | 0.0001        | -               |
+| 1.1513  | 700     | 0.0001        | -               |
+| 1.2336  | 750     | 0.0001        | -               |
+| 1.3158  | 800     | 0.0001        | -               |
+| 1.3980  | 850     | 0.0001        | -               |
+| 1.4803  | 900     | 0.0001        | -               |
+| 1.5625  | 950     | 0.0001        | -               |
+| 1.6447  | 1000    | 0.0001        | -               |
+| 1.7270  | 1050    | 0.0001        | -               |
+| 1.8092  | 1100    | 0.0           | -               |
+| 1.8914  | 1150    | 0.0001        | -               |
+| 1.9737  | 1200    | 0.0001        | -               |
+| 2.0     | 1216    | -             | 0.2807          |
+| 2.0559  | 1250    | 0.0001        | -               |
+| 2.1382  | 1300    | 0.0001        | -               |
+| 2.2204  | 1350    | 0.0001        | -               |
+| 2.3026  | 1400    | 0.0           | -               |
+| 2.3849  | 1450    | 0.0001        | -               |
+| 2.4671  | 1500    | 0.0001        | -               |
+| 2.5493  | 1550    | 0.0           | -               |
+| 2.6316  | 1600    | 0.0001        | -               |
+| 2.7138  | 1650    | 0.0           | -               |
+| 2.7961  | 1700    | 0.0001        | -               |
+| 2.8783  | 1750    | 0.0           | -               |
+| 2.9605  | 1800    | 0.0           | -               |
+| 3.0     | 1824    | -             | 0.3011          |
+| 3.0428  | 1850    | 0.0           | -               |
+| 3.125   | 1900    | 0.0001        | -               |
+| 3.2072  | 1950    | 0.0001        | -               |
+| 3.2895  | 2000    | 0.0           | -               |
+| 3.3717  | 2050    | 0.0001        | -               |
+| 3.4539  | 2100    | 0.0001        | -               |
+| 3.5362  | 2150    | 0.0           | -               |
+| 3.6184  | 2200    | 0.0001        | -               |
+| 3.7007  | 2250    | 0.0001        | -               |
+| 3.7829  | 2300    | 0.0           | -               |
+| 3.8651  | 2350    | 0.0           | -               |
+| 3.9474  | 2400    | 0.0001        | -               |
+| 4.0     | 2432    | -             | 0.311           |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.9.16
+- SetFit: 1.0.3
+- Sentence Transformers: 2.2.2
+- Transformers: 4.35.2
+- PyTorch: 2.1.1
+- 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": "checkpoints/step_608/",
+  "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": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

config_setfit.json

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+{
+  "labels": [
+    "negative",
+    "positive"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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+oid sha256:e6edab69eb7383a345b971f1cc111ac0895bbb9ddf57d973d4ac9190ec929163
+size 437967672

model_head.pkl

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+oid sha256:f35e6601b6fc31203fb7fb7698a4679e65aa29287130a5450c65ac5a56bab741
+size 25815

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"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "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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+      "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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+      "special": true
+    },
+    "30526": {
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "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]"
+}