Add SetFit model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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/paraphrase-mpnet-base-v2
+metrics:
+- accuracy
+widget:
+- text: What is the process for exchanging sneakers?
+- text: Can I track the delivery status of my order using the store's customer service
+    hotline?
+- text: What are the latest choker styles available for a wedding occasion?
+- text: I'm interested in pendants that can be engraved. Do you provide such services?
+- text: Do you offer weekend or holiday deliveries for orders?
+pipeline_tag: text-classification
+inference: true
+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: 0.9276729559748428
+      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:** 6 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+|:------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| complaints              | <ul><li>"I recently purchased the Teddy's Heartbeat Gold Pendant and I'm disappointed to see that the pendant scratches very easily. Is there anything that can be done about this?"</li><li>'I recently purchased the Pearly Round Earrings, but upon arrival, I noticed that the pearls are scratched and lack luster. This is not what I expected based on the product images online.'</li><li>'The Pearl Grace Ring I received has lost its shine within a week of purchase, which is disappointing. What can be done about this?'</li></ul> |
+| product discoverability | <ul><li>'Customized bakery boxes for specific needs'</li><li>'Suggest me some casual sneakers for women'</li><li>'Do you have Converse sneakers in different colors?'</li></ul>                                                                                                                                                                                                                                                                                                                                                                  |
+| order tracking          | <ul><li>"I'm concerned about the delay in the delivery of my order. Can you update me on the status?"</li><li>'I need to confirm the dispatch date for my recent purchase. Can you help me with that?'</li><li>'I want to know the status of my recent order. Can you provide me with the current shipping information?'</li></ul>                                                                                                                                                                                                               |
+| product discoveribility | <ul><li>'Do you have any charm bracelets available at your store?'</li><li>'Do you have any statement Earrings that would be suitable for a wedding?'</li><li>'Do you have any choker-style necklaces that are trending right now?'</li></ul>                                                                                                                                                                                                                                                                                                    |
+| product faq             | <ul><li>'What is the material used for the red round decorative tin box for wholesale orders?'</li><li>'Are the Adidas Yeezy Foam RNNR MX Cinder unisex?'</li><li>'Is the pack of 50 popcorn boxes available in different colors?'</li></ul>                                                                                                                                                                                                                                                                                                     |
+| product policy          | <ul><li>'How can I find out my ring size before placing an order?'</li><li>'What is the warranty on candle supplies?'</li><li>'Can I get a refund for a necklace if it has a manufacturing defect?'</li></ul>                                                                                                                                                                                                                                                                                                                                    |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9277   |
+
+## 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("What is the process for exchanging sneakers?")
+```
+
+<!--
+### 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   | 4   | 15.4667 | 37  |
+
+| Label                   | Training Sample Count |
+|:------------------------|:----------------------|
+| complaints              | 20                    |
+| order tracking          | 20                    |
+| product discoverability | 20                    |
+| product discoveribility | 20                    |
+| product faq             | 20                    |
+| product policy          | 20                    |
+
+### 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0013 | 1    | 0.2116        | -               |
+| 0.0667 | 50   | 0.1402        | -               |
+| 0.1333 | 100  | 0.1163        | -               |
+| 0.2    | 150  | 0.024         | -               |
+| 0.2667 | 200  | 0.0037        | -               |
+| 0.3333 | 250  | 0.0016        | -               |
+| 0.4    | 300  | 0.0011        | -               |
+| 0.4667 | 350  | 0.0008        | -               |
+| 0.5333 | 400  | 0.0004        | -               |
+| 0.6    | 450  | 0.0005        | -               |
+| 0.6667 | 500  | 0.001         | -               |
+| 0.7333 | 550  | 0.0002        | -               |
+| 0.8    | 600  | 0.0002        | -               |
+| 0.8667 | 650  | 0.0003        | -               |
+| 0.9333 | 700  | 0.0002        | -               |
+| 1.0    | 750  | 0.0002        | -               |
+| 1.0667 | 800  | 0.0002        | -               |
+| 1.1333 | 850  | 0.0002        | -               |
+| 1.2    | 900  | 0.0001        | -               |
+| 1.2667 | 950  | 0.0001        | -               |
+| 1.3333 | 1000 | 0.0002        | -               |
+| 1.4    | 1050 | 0.0001        | -               |
+| 1.4667 | 1100 | 0.0001        | -               |
+| 1.5333 | 1150 | 0.0001        | -               |
+| 1.6    | 1200 | 0.0002        | -               |
+| 1.6667 | 1250 | 0.0001        | -               |
+| 1.7333 | 1300 | 0.0001        | -               |
+| 1.8    | 1350 | 0.0002        | -               |
+| 1.8667 | 1400 | 0.0001        | -               |
+| 1.9333 | 1450 | 0.0001        | -               |
+| 2.0    | 1500 | 0.0001        | -               |
+| 2.0667 | 1550 | 0.0001        | -               |
+| 2.1333 | 1600 | 0.0002        | -               |
+| 2.2    | 1650 | 0.0001        | -               |
+| 2.2667 | 1700 | 0.0001        | -               |
+| 2.3333 | 1750 | 0.0001        | -               |
+| 2.4    | 1800 | 0.0001        | -               |
+| 2.4667 | 1850 | 0.0001        | -               |
+| 2.5333 | 1900 | 0.0001        | -               |
+| 2.6    | 1950 | 0.0001        | -               |
+| 2.6667 | 2000 | 0.0001        | -               |
+| 2.7333 | 2050 | 0.0001        | -               |
+| 2.8    | 2100 | 0.0001        | -               |
+| 2.8667 | 2150 | 0.0001        | -               |
+| 2.9333 | 2200 | 0.0001        | -               |
+| 3.0    | 2250 | 0.0001        | -               |
+| 3.0667 | 2300 | 0.0001        | -               |
+| 3.1333 | 2350 | 0.0001        | -               |
+| 3.2    | 2400 | 0.0001        | -               |
+| 3.2667 | 2450 | 0.0001        | -               |
+| 3.3333 | 2500 | 0.0001        | -               |
+| 3.4    | 2550 | 0.0001        | -               |
+| 3.4667 | 2600 | 0.0001        | -               |
+| 3.5333 | 2650 | 0.0001        | -               |
+| 3.6    | 2700 | 0.0001        | -               |
+| 3.6667 | 2750 | 0.0001        | -               |
+| 3.7333 | 2800 | 0.0001        | -               |
+| 3.8    | 2850 | 0.0001        | -               |
+| 3.8667 | 2900 | 0.0001        | -               |
+| 3.9333 | 2950 | 0.0001        | -               |
+| 4.0    | 3000 | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.9.16
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.40.1
+- PyTorch: 2.3.0
+- Datasets: 2.19.0
+- 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/paraphrase-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": "4.40.1",
+  "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"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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+{
+  "labels": [
+    "complaints",
+    "order tracking",
+    "product discoverability",
+    "product discoveribility",
+    "product faq",
+    "product policy"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9cf05a6ea6f3d6bc0260b7b8fa89947d13e691d4dffa11ee25937ff6da3fa891
+size 437967672

model_head.pkl

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+oid sha256:e6a802549e27335ab3dee1b924ba13d3f033957dbe3522c02056ba823944040d
+size 38311

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
+    },
+    "1": {
+      "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
+    },
+    "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_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}