| | --- |
| | 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.* |
| | --> |
