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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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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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datasets: |
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- go_emotions |
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metrics: |
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- accuracy |
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widget: |
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- text: Curious as to why he's been passed up so many times now. |
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- text: I think you mean the announcement |
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- text: try to attract the guy that i like. other than that i love gaming drawing |
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writing and watching tv. |
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- text: I thought that phrase was only used for memes now lol at least that's what |
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I got from Vic deals |
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- text: 'Fantastic read, thanks for the insights! ' |
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pipeline_tag: text-classification |
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inference: false |
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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 trained on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset 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 OneVsRestClassifier 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 OneVsRestClassifier instance |
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- **Maximum Sequence Length:** 512 tokens |
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<!-- - **Number of Classes:** Unknown --> |
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- **Training Dataset:** [go_emotions](https://huggingface.co/datasets/go_emotions) |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** 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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## 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("bhaskars113/go-emotions-multilabel") |
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# Run inference |
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preds = model("I think you mean the announcement") |
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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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*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 | 1 | 13.6060 | 30 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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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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| 0.0004 | 1 | 0.3873 | - | |
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| 0.0223 | 50 | 0.2243 | - | |
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| 0.0446 | 100 | 0.2305 | - | |
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| 0.0670 | 150 | 0.2297 | - | |
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| 0.0893 | 200 | 0.2758 | - | |
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| 0.1116 | 250 | 0.2197 | - | |
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| 0.1339 | 300 | 0.1984 | - | |
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| 0.1562 | 350 | 0.1729 | - | |
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| 0.1786 | 400 | 0.1244 | - | |
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| 0.2009 | 450 | 0.164 | - | |
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| 0.2232 | 500 | 0.1587 | - | |
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| 0.2455 | 550 | 0.2272 | - | |
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| 0.2679 | 600 | 0.3367 | - | |
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| 0.2902 | 650 | 0.1715 | - | |
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| 0.3125 | 700 | 0.2213 | - | |
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| 0.3348 | 750 | 0.2394 | - | |
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| 0.3571 | 800 | 0.1275 | - | |
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| 0.3795 | 850 | 0.1919 | - | |
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| 0.4018 | 900 | 0.143 | - | |
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| 0.4241 | 950 | 0.2431 | - | |
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| 0.4464 | 1000 | 0.1747 | - | |
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| 0.4688 | 1050 | 0.1567 | - | |
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| 0.4911 | 1100 | 0.194 | - | |
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| 0.5134 | 1150 | 0.1895 | - | |
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| 0.5357 | 1200 | 0.1601 | - | |
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| 0.5580 | 1250 | 0.1042 | - | |
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| 0.5804 | 1300 | 0.0553 | - | |
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| 0.6027 | 1350 | 0.1614 | - | |
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| 0.625 | 1400 | 0.1854 | - | |
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| 0.6473 | 1450 | 0.1259 | - | |
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| 0.6696 | 1500 | 0.138 | - | |
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| 0.6920 | 1550 | 0.2181 | - | |
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| 0.7143 | 1600 | 0.1144 | - | |
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| 0.7366 | 1650 | 0.1987 | - | |
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| 0.7589 | 1700 | 0.0859 | - | |
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| 0.7812 | 1750 | 0.1665 | - | |
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| 0.8036 | 1800 | 0.1628 | - | |
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| 0.8259 | 1850 | 0.2296 | - | |
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| 0.8482 | 1900 | 0.1892 | - | |
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| 0.8705 | 1950 | 0.2033 | - | |
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| 0.8929 | 2000 | 0.1507 | - | |
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| 0.9152 | 2050 | 0.1592 | - | |
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| 0.9375 | 2100 | 0.1077 | - | |
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| 0.9598 | 2150 | 0.1415 | - | |
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| 0.9821 | 2200 | 0.1561 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.7.0 |
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- Transformers: 4.40.0 |
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- PyTorch: 2.2.1+cu121 |
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- Datasets: 2.19.0 |
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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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