| | --- |
| | base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
| | library_name: setfit |
| | metrics: |
| | - accuracy |
| | pipeline_tag: text-classification |
| | tags: |
| | - setfit |
| | - sentence-transformers |
| | - text-classification |
| | - generated_from_setfit_trainer |
| | widget: |
| | - text: Do you have the Nike Blazer Mid sacai Snow Beach in size 9? |
| | - text: How can I adapt K-beauty routines for dry weather? |
| | - text: I like to listen to classical music |
| | - text: If this product is for weight management, what is the sub-category? |
| | - text: How long does it take to receive a refund after returning a product? |
| | 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: 0.8711340206185567 |
| | 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 | |
| | |:------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | product policy | <ul><li>'Do you offer a gift wrapping service for sneakers?'</li><li>'What are the consequences if my account is suspended or terminated for any reason?'</li><li>'Do you share my personal information with third parties?'</li></ul> | |
| | | general faq | <ul><li>'Can you explain why Mashru silk is considered more comfortable to wear compared to pure silk sarees?'</li><li>'What are some tips for maximizing the antioxidant content when brewing green tea?'</li><li>'Can you recommend K-beauty products for hot and humid climates?'</li></ul> | |
| | | product discoverability | <ul><li>'Are there any sarees with Kadwa Weave technique?'</li><li>'cookie boxes with dividers'</li><li>'Are there any products for dry skin?'</li></ul> | |
| | | Out of Scope | <ul><li>'Is this website secure?'</li><li>'How do you handle intellectual property disputes?'</li><li>'Do you know how to play the piano?'</li></ul> | |
| | | order tracking | <ul><li>'I want to deliver candle supplies to Jaipur, how many days will it take to deliver?'</li><li>'I want to deliver bags to Pune, how many days will it take to deliver?'</li><li>'I need to change the delivery address for my recent order, how can I do that?'</li></ul> | |
| | | product faq | <ul><li>'Does this product help with dark spots?'</li><li>'3. Is this product currently in stock?'</li><li>'Is the product in stock?'</li></ul> | |
| |
|
| | ## Evaluation |
| |
|
| | ### Metrics |
| | | Label | Accuracy | |
| | |:--------|:---------| |
| | | **all** | 0.8711 | |
| |
|
| | ## 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 like to listen to classical music") |
| | ``` |
| |
|
| | <!-- |
| | ### 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 | 10.66 | 28 | |
| |
|
| | | Label | Training Sample Count | |
| | |:------------------------|:----------------------| |
| | | Out of Scope | 50 | |
| | | general faq | 50 | |
| | | order tracking | 50 | |
| | | product discoverability | 50 | |
| | | product faq | 50 | |
| | | product policy | 50 | |
| |
|
| | ### 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 | |
| | |:------:|:----:|:-------------:|:---------------:| |
| | | 0.0002 | 1 | 0.2592 | - | |
| | | 0.0107 | 50 | 0.2424 | - | |
| | | 0.0213 | 100 | 0.1506 | - | |
| | | 0.0320 | 150 | 0.222 | - | |
| | | 0.0427 | 200 | 0.1227 | - | |
| | | 0.0533 | 250 | 0.1801 | - | |
| | | 0.0640 | 300 | 0.1111 | - | |
| | | 0.0747 | 350 | 0.0346 | - | |
| | | 0.0853 | 400 | 0.0313 | - | |
| | | 0.0960 | 450 | 0.0048 | - | |
| | | 0.1067 | 500 | 0.0023 | - | |
| | | 0.1173 | 550 | 0.0018 | - | |
| | | 0.1280 | 600 | 0.0133 | - | |
| | | 0.1387 | 650 | 0.0008 | - | |
| | | 0.1493 | 700 | 0.0006 | - | |
| | | 0.1600 | 750 | 0.0005 | - | |
| | | 0.1706 | 800 | 0.0008 | - | |
| | | 0.1813 | 850 | 0.0007 | - | |
| | | 0.1920 | 900 | 0.0006 | - | |
| | | 0.2026 | 950 | 0.0006 | - | |
| | | 0.2133 | 1000 | 0.0003 | - | |
| | | 0.2240 | 1050 | 0.0026 | - | |
| | | 0.2346 | 1100 | 0.0004 | - | |
| | | 0.2453 | 1150 | 0.0004 | - | |
| | | 0.2560 | 1200 | 0.0004 | - | |
| | | 0.2666 | 1250 | 0.0005 | - | |
| | | 0.2773 | 1300 | 0.0005 | - | |
| | | 0.2880 | 1350 | 0.0003 | - | |
| | | 0.2986 | 1400 | 0.0001 | - | |
| | | 0.3093 | 1450 | 0.0001 | - | |
| | | 0.3200 | 1500 | 0.0002 | - | |
| | | 0.3306 | 1550 | 0.0002 | - | |
| | | 0.3413 | 1600 | 0.0002 | - | |
| | | 0.3520 | 1650 | 0.0001 | - | |
| | | 0.3626 | 1700 | 0.0004 | - | |
| | | 0.3733 | 1750 | 0.0002 | - | |
| | | 0.3840 | 1800 | 0.0005 | - | |
| | | 0.3946 | 1850 | 0.0002 | - | |
| | | 0.4053 | 1900 | 0.0002 | - | |
| | | 0.4160 | 1950 | 0.0001 | - | |
| | | 0.4266 | 2000 | 0.0001 | - | |
| | | 0.4373 | 2050 | 0.0001 | - | |
| | | 0.4480 | 2100 | 0.0001 | - | |
| | | 0.4586 | 2150 | 0.0001 | - | |
| | | 0.4693 | 2200 | 0.0002 | - | |
| | | 0.4799 | 2250 | 0.0048 | - | |
| | | 0.4906 | 2300 | 0.0001 | - | |
| | | 0.5013 | 2350 | 0.001 | - | |
| | | 0.5119 | 2400 | 0.0002 | - | |
| | | 0.5226 | 2450 | 0.0002 | - | |
| | | 0.5333 | 2500 | 0.0001 | - | |
| | | 0.5439 | 2550 | 0.0001 | - | |
| | | 0.5546 | 2600 | 0.0001 | - | |
| | | 0.5653 | 2650 | 0.0001 | - | |
| | | 0.5759 | 2700 | 0.0001 | - | |
| | | 0.5866 | 2750 | 0.0001 | - | |
| | | 0.5973 | 2800 | 0.0001 | - | |
| | | 0.6079 | 2850 | 0.0001 | - | |
| | | 0.6186 | 2900 | 0.0001 | - | |
| | | 0.6293 | 2950 | 0.0001 | - | |
| | | 0.6399 | 3000 | 0.0001 | - | |
| | | 0.6506 | 3050 | 0.0001 | - | |
| | | 0.6613 | 3100 | 0.0001 | - | |
| | | 0.6719 | 3150 | 0.0001 | - | |
| | | 0.6826 | 3200 | 0.0001 | - | |
| | | 0.6933 | 3250 | 0.0001 | - | |
| | | 0.7039 | 3300 | 0.0001 | - | |
| | | 0.7146 | 3350 | 0.0001 | - | |
| | | 0.7253 | 3400 | 0.0001 | - | |
| | | 0.7359 | 3450 | 0.0001 | - | |
| | | 0.7466 | 3500 | 0.0001 | - | |
| | | 0.7573 | 3550 | 0.0001 | - | |
| | | 0.7679 | 3600 | 0.0001 | - | |
| | | 0.7786 | 3650 | 0.0001 | - | |
| | | 0.7892 | 3700 | 0.0001 | - | |
| | | 0.7999 | 3750 | 0.0001 | - | |
| | | 0.8106 | 3800 | 0.0001 | - | |
| | | 0.8212 | 3850 | 0.0 | - | |
| | | 0.8319 | 3900 | 0.0001 | - | |
| | | 0.8426 | 3950 | 0.0001 | - | |
| | | 0.8532 | 4000 | 0.0001 | - | |
| | | 0.8639 | 4050 | 0.0001 | - | |
| | | 0.8746 | 4100 | 0.0001 | - | |
| | | 0.8852 | 4150 | 0.0 | - | |
| | | 0.8959 | 4200 | 0.0001 | - | |
| | | 0.9066 | 4250 | 0.0001 | - | |
| | | 0.9172 | 4300 | 0.0001 | - | |
| | | 0.9279 | 4350 | 0.0001 | - | |
| | | 0.9386 | 4400 | 0.0001 | - | |
| | | 0.9492 | 4450 | 0.0001 | - | |
| | | 0.9599 | 4500 | 0.0001 | - | |
| | | 0.9706 | 4550 | 0.0001 | - | |
| | | 0.9812 | 4600 | 0.0 | - | |
| | | 0.9919 | 4650 | 0.0001 | - | |
| | | 1.0026 | 4700 | 0.0 | - | |
| | | 1.0132 | 4750 | 0.0001 | - | |
| | | 1.0239 | 4800 | 0.0001 | - | |
| | | 1.0346 | 4850 | 0.0001 | - | |
| | | 1.0452 | 4900 | 0.0001 | - | |
| | | 1.0559 | 4950 | 0.0001 | - | |
| | | 1.0666 | 5000 | 0.0 | - | |
| | | 1.0772 | 5050 | 0.0 | - | |
| | | 1.0879 | 5100 | 0.0001 | - | |
| | | 1.0985 | 5150 | 0.0 | - | |
| | | 1.1092 | 5200 | 0.0 | - | |
| | | 1.1199 | 5250 | 0.0 | - | |
| | | 1.1305 | 5300 | 0.0001 | - | |
| | | 1.1412 | 5350 | 0.0001 | - | |
| | | 1.1519 | 5400 | 0.0 | - | |
| | | 1.1625 | 5450 | 0.0001 | - | |
| | | 1.1732 | 5500 | 0.0001 | - | |
| | | 1.1839 | 5550 | 0.0002 | - | |
| | | 1.1945 | 5600 | 0.0 | - | |
| | | 1.2052 | 5650 | 0.0 | - | |
| | | 1.2159 | 5700 | 0.0 | - | |
| | | 1.2265 | 5750 | 0.0 | - | |
| | | 1.2372 | 5800 | 0.0001 | - | |
| | | 1.2479 | 5850 | 0.0001 | - | |
| | | 1.2585 | 5900 | 0.0001 | - | |
| | | 1.2692 | 5950 | 0.0 | - | |
| | | 1.2799 | 6000 | 0.0 | - | |
| | | 1.2905 | 6050 | 0.0 | - | |
| | | 1.3012 | 6100 | 0.0001 | - | |
| | | 1.3119 | 6150 | 0.0 | - | |
| | | 1.3225 | 6200 | 0.0 | - | |
| | | 1.3332 | 6250 | 0.0 | - | |
| | | 1.3439 | 6300 | 0.0 | - | |
| | | 1.3545 | 6350 | 0.0 | - | |
| | | 1.3652 | 6400 | 0.0 | - | |
| | | 1.3759 | 6450 | 0.0 | - | |
| | | 1.3865 | 6500 | 0.0 | - | |
| | | 1.3972 | 6550 | 0.0 | - | |
| | | 1.4078 | 6600 | 0.0 | - | |
| | | 1.4185 | 6650 | 0.0 | - | |
| | | 1.4292 | 6700 | 0.0 | - | |
| | | 1.4398 | 6750 | 0.0 | - | |
| | | 1.4505 | 6800 | 0.0 | - | |
| | | 1.4612 | 6850 | 0.0 | - | |
| | | 1.4718 | 6900 | 0.0001 | - | |
| | | 1.4825 | 6950 | 0.0001 | - | |
| | | 1.4932 | 7000 | 0.0 | - | |
| | | 1.5038 | 7050 | 0.0 | - | |
| | | 1.5145 | 7100 | 0.0001 | - | |
| | | 1.5252 | 7150 | 0.0001 | - | |
| | | 1.5358 | 7200 | 0.0001 | - | |
| | | 1.5465 | 7250 | 0.0001 | - | |
| | | 1.5572 | 7300 | 0.0 | - | |
| | | 1.5678 | 7350 | 0.0 | - | |
| | | 1.5785 | 7400 | 0.0 | - | |
| | | 1.5892 | 7450 | 0.0001 | - | |
| | | 1.5998 | 7500 | 0.0 | - | |
| | | 1.6105 | 7550 | 0.0 | - | |
| | | 1.6212 | 7600 | 0.0 | - | |
| | | 1.6318 | 7650 | 0.0 | - | |
| | | 1.6425 | 7700 | 0.0 | - | |
| | | 1.6532 | 7750 | 0.0 | - | |
| | | 1.6638 | 7800 | 0.0 | - | |
| | | 1.6745 | 7850 | 0.0 | - | |
| | | 1.6852 | 7900 | 0.0 | - | |
| | | 1.6958 | 7950 | 0.0 | - | |
| | | 1.7065 | 8000 | 0.0 | - | |
| | | 1.7172 | 8050 | 0.0 | - | |
| | | 1.7278 | 8100 | 0.0 | - | |
| | | 1.7385 | 8150 | 0.0001 | - | |
| | | 1.7491 | 8200 | 0.0 | - | |
| | | 1.7598 | 8250 | 0.0 | - | |
| | | 1.7705 | 8300 | 0.0 | - | |
| | | 1.7811 | 8350 | 0.0001 | - | |
| | | 1.7918 | 8400 | 0.0 | - | |
| | | 1.8025 | 8450 | 0.0 | - | |
| | | 1.8131 | 8500 | 0.0 | - | |
| | | 1.8238 | 8550 | 0.0 | - | |
| | | 1.8345 | 8600 | 0.0001 | - | |
| | | 1.8451 | 8650 | 0.0 | - | |
| | | 1.8558 | 8700 | 0.0 | - | |
| | | 1.8665 | 8750 | 0.0001 | - | |
| | | 1.8771 | 8800 | 0.0 | - | |
| | | 1.8878 | 8850 | 0.0 | - | |
| | | 1.8985 | 8900 | 0.0 | - | |
| | | 1.9091 | 8950 | 0.0001 | - | |
| | | 1.9198 | 9000 | 0.0 | - | |
| | | 1.9305 | 9050 | 0.0 | - | |
| | | 1.9411 | 9100 | 0.0 | - | |
| | | 1.9518 | 9150 | 0.0 | - | |
| | | 1.9625 | 9200 | 0.0 | - | |
| | | 1.9731 | 9250 | 0.0 | - | |
| | | 1.9838 | 9300 | 0.0 | - | |
| | | 1.9945 | 9350 | 0.0 | - | |
| |
|
| | ### Framework Versions |
| | - Python: 3.10.16 |
| | - 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.* |
| | --> |
| |
|
| | <!-- |
| | ## 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.* |
| | --> |
