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--- |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: Are there any shipping charges for prepaid orders within India? |
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- text: What is included in the Ultimate Shoe Cleaning Kit? |
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- text: Are there any products for fighting odor in suede shoes? |
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- text: What is included in the Ultimate Shoe Cleaning Kit + Fresh - Shoe Deodorizer |
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combo? |
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- text: What type of material is the Crease Defender designed for? |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.8695652173913043 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 5 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| product faq | <ul><li>'Does the Safe - Dual Doors Sneaker Crates offer protection for shoes?'</li><li>'What is the price of the Microfiber Towel (Pack of 3 & 5)?'</li><li>'How many Crease Defenders are included in the pack of 2?'</li></ul> | |
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| general faq | <ul><li>'What makes SHOEGR Safe - Dual Door Sneaker Crates different from other shoe storage solutions?'</li><li>'How should I store my shoes to keep them in good condition?'</li><li>'What can I do to keep my shoes smelling fresh?'</li></ul> | |
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| order tracking | <ul><li>"My order was supposed to arrive yesterday but it hasn't. Can you check the delivery status for me?"</li><li>'I ordered the Cupcake Cases 3 days ago with order no 34567 how long will it take to deliver?'</li><li>'What is the expected delivery time for the Baking Ingredients I ordered?'</li></ul> | |
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| product policy | <ul><li>'What happens if I breach any of the Terms of Service?'</li><li>'How long do I have to replace a product after delivery?'</li><li>'What are the different types of cookies used on your site and their purposes?'</li></ul> | |
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| product discoverability | <ul><li>'Do you carry products for cleaning mesh shoes?'</li><li>'What are the prices for products designed for all types of materials?'</li><li>'Are there any single-product options for shoe maintenance?'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.8696 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("setfit_model_id") |
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# Run inference |
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preds = model("What is included in the Ultimate Shoe Cleaning Kit?") |
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``` |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 4 | 11.7841 | 24 | |
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| Label | Training Sample Count | |
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|:------------------------|:----------------------| |
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| general faq | 12 | |
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| order tracking | 24 | |
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| product discoverability | 15 | |
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| product faq | 21 | |
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| product policy | 16 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (2, 2) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0026 | 1 | 0.1187 | - | |
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| 0.1309 | 50 | 0.0867 | - | |
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| 0.2618 | 100 | 0.0003 | - | |
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| 0.3927 | 150 | 0.0002 | - | |
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| 0.5236 | 200 | 0.0001 | - | |
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| 0.6545 | 250 | 0.0002 | - | |
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| 0.7853 | 300 | 0.0001 | - | |
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| 0.9162 | 350 | 0.0001 | - | |
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| 1.0471 | 400 | 0.0001 | - | |
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| 1.1780 | 450 | 0.0001 | - | |
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| 1.3089 | 500 | 0.0001 | - | |
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| 1.4398 | 550 | 0.0001 | - | |
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| 1.5707 | 600 | 0.0001 | - | |
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| 1.7016 | 650 | 0.0001 | - | |
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| 1.8325 | 700 | 0.0001 | - | |
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| 1.9634 | 750 | 0.0001 | - | |
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### Framework Versions |
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- Python: 3.10.16 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.7.0 |
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- Transformers: 4.40.2 |
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- PyTorch: 2.2.2 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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