| | --- |
| | 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 makeup products do you have for eyes? |
| | - text: How can I prevent acne if I have oily skin? |
| | - text: What is the estimated delivery time for orders within the same country? |
| | - text: Can you recommend a good moisturizer for winter skin care? |
| | - text: Is the Beachy-Floral-Citrus Mini Eau De Parfum Gift Set suitable for all skin |
| | types? |
| | 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: 0.9166666666666666 |
| | 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:** 5 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 discoverability | <ul><li>'Can you show me all the products for oily skin?'</li><li>'Do you have any makeup remover?'</li><li>'Can you show me all the products for dark spots?'</li></ul> | |
| | | order tracking | <ul><li>'What is the estimated delivery time for orders within the same state?'</li><li>'I need to know the status of my recent order. Can you check if it has been dispatched?'</li><li>'I ordered the Cake Decorating Kit 4 days ago, can you provide the tracking information?'</li></ul> | |
| | | product faq | <ul><li>'What are the different shades available in the Color Affair Nail Polish Pixie Dust Collection?'</li><li>'Is the Touch-N-Go Lip & Cheek Tint a vegan and cruelty-free product?'</li><li>'Is this product suitable for oily skin?'</li></ul> | |
| | | general faq | <ul><li>'How often should I use exfoliants to reduce open pores?'</li><li>'What are the most effective ingredients for treating acne?'</li><li>'Are home remedies effective for severe acne?'</li></ul> | |
| | | product policy | <ul><li>'Are your products suitable for sensitive skin?'</li><li>'How can I track my order on the Plum Goodness app?'</li><li>'What is the contact number for customer support?'</li></ul> | |
| |
|
| | ## Evaluation |
| |
|
| | ### Metrics |
| | | Label | Accuracy | |
| | |:--------|:---------| |
| | | **all** | 0.9167 | |
| |
|
| | ## 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 makeup products do you have for eyes?") |
| | ``` |
| |
|
| | <!-- |
| | ### 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 | 11.0 | 24 | |
| |
|
| | | Label | Training Sample Count | |
| | |:------------------------|:----------------------| |
| | | general faq | 20 | |
| | | order tracking | 24 | |
| | | product discoverability | 16 | |
| | | product faq | 24 | |
| | | product policy | 12 | |
| |
|
| | ### Training Hyperparameters |
| | - batch_size: (16, 16) |
| | - num_epochs: (2, 2) |
| | - 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 | |
| | |:------:|:----:|:-------------:|:---------------:| |
| | | 0.0022 | 1 | 0.2082 | - | |
| | | 0.1101 | 50 | 0.1229 | - | |
| | | 0.2203 | 100 | 0.0262 | - | |
| | | 0.3304 | 150 | 0.0015 | - | |
| | | 0.4405 | 200 | 0.001 | - | |
| | | 0.5507 | 250 | 0.0008 | - | |
| | | 0.6608 | 300 | 0.0005 | - | |
| | | 0.7709 | 350 | 0.0004 | - | |
| | | 0.8811 | 400 | 0.0003 | - | |
| | | 0.9912 | 450 | 0.0003 | - | |
| | | 1.1013 | 500 | 0.0002 | - | |
| | | 1.2115 | 550 | 0.0002 | - | |
| | | 1.3216 | 600 | 0.0004 | - | |
| | | 1.4317 | 650 | 0.0002 | - | |
| | | 1.5419 | 700 | 0.0003 | - | |
| | | 1.6520 | 750 | 0.0002 | - | |
| | | 1.7621 | 800 | 0.0002 | - | |
| | | 1.8722 | 850 | 0.0002 | - | |
| | | 1.9824 | 900 | 0.0003 | - | |
| |
|
| | ### Framework Versions |
| | - Python: 3.9.19 |
| | - SetFit: 1.0.3 |
| | - Sentence Transformers: 2.7.0 |
| | - Transformers: 4.40.2 |
| | - PyTorch: 2.2.2 |
| | - Datasets: 2.19.1 |
| | - 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.* |
| | --> |
| |
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| | <!-- |
| | ## Model Card Authors |
| |
|
| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| | --> |
| |
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| | <!-- |
| | ## Model Card Contact |
| |
|
| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| | --> |
