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--- |
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library_name: setfit |
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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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metrics: |
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- accuracy |
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widget: |
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- text: '"She sums up this theme with this pithy quote: \\\\\The first two letters |
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of Fundamentalist are F-U.\\\\\"" However, these are the parents who drop everything |
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to spend weeks caring for her when she is near death and all the friends from |
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her newly emancipated life are missing in action."""' |
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- text: This was a christmas gift for my son , he couldn't wait to get it home and |
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set it up and watch movies and play video games on it...when he did he was speachless. |
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- text: The shorts are made out of a good, durable material but not so stiff that |
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you feel like you can't move. |
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- text: The only problem I knew I was going to have upfront was on the reciever end |
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of things as I needed an extra set of speaker outputs,but the Monoprice 4ch speaker |
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selector (#9995) took care of that deal. |
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- text: This book is so good, I have to rave! |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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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: 0.7018 |
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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:** 2 classes |
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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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| 0 | <ul><li>'Bank and Gaza city of thompson falls in love with your kindle fire and rescue plan for the most important thing is the kindle fire department for sure to check it out for sure to use none of them'</li><li>"I really wanted green, really wanted beige, really wanted white didn't get any of those colors I got grey brown red instead."</li><li>'My only complaint is that I would like more colours please!!!'</li></ul> | |
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| 1 | <ul><li>'I WOULD BUY MORE IN THE FUTURE FOR MY OWN REAL TREE.'</li><li>'I had to give this item one star but if there was a negative 5 star rating I would have chosen that.'</li><li>'The only thing I would have like for it to have a hole in the middle so I can put the stopper in without removing the mat.'</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** | 0.7018 | |
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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("vidhi0206/setfit-paraphrase-mpnet-amazoncf") |
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# Run inference |
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preds = model("This book is so good, I have to rave!") |
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``` |
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## Bias, Risks and Limitations |
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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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## 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 | 9 | 19.875 | 39 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 8 | |
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| 1 | 8 | |
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### Training Hyperparameters |
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- batch_size: (8, 8) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-05 |
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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: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0125 | 1 | 0.1979 | - | |
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| 0.625 | 50 | 0.0022 | - | |
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### Framework Versions |
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- Python: 3.8.10 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.3.1 |
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- Transformers: 4.37.2 |
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- PyTorch: 2.2.0+cu121 |
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- Datasets: 2.17.0 |
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- Tokenizers: 0.15.1 |
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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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