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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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datasets: |
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- SetFit/sst2 |
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metrics: |
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- accuracy |
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widget: |
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- text: a noble failure . |
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- text: ms. seigner and mr. serrault bring fresh , unforced naturalism to their characters |
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. |
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- text: 'nothing can detract from the affection of that moral favorite : friends will |
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be friends through thick and thin .' |
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- text: confuses its message with an ultimate desire to please , and contorting itself |
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into an idea of expectation is the last thing any of these three actresses , nor |
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their characters , deserve . |
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- text: despite its promising cast of characters , big trouble remains a loosely tied |
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series of vignettes which only prove that ` zany ' does n't necessarily mean ` |
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funny . ' |
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pipeline_tag: text-classification |
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inference: true |
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base_model: BAAI/bge-small-en-v1.5 |
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model-index: |
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- name: SetFit with BAAI/bge-small-en-v1.5 |
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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: SetFit/sst2 |
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type: SetFit/sst2 |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.8841743119266054 |
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name: Accuracy |
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--- |
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# SetFit with BAAI/bge-small-en-v1.5 |
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This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [SetFit/sst2](https://huggingface.co/datasets/SetFit/sst2) dataset that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |
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- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 2 classes |
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- **Training Dataset:** [SetFit/sst2](https://huggingface.co/datasets/SetFit/sst2) |
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<!-- - **Language:** 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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### Model Labels |
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| Label | Examples | |
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|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 1 | <ul><li>'a stirring , funny and finally transporting re-imagining of beauty and the beast and 1930s horror films'</li><li>'this is a visually stunning rumination on love , memory , history and the war between art and commerce .'</li><li>"jonathan parker 's bartleby should have been the be-all-end-all of the modern-office anomie films ."</li></ul> | |
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| 0 | <ul><li>'apparently reassembled from the cutting-room floor of any given daytime soap .'</li><li>"they presume their audience wo n't sit still for a sociology lesson , however entertainingly presented , so they trot out the conventional science-fiction elements of bug-eyed monsters and futuristic women in skimpy clothes ."</li><li>'a fan film that for the uninitiated plays better on video with the sound turned down .'</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** | 0.8842 | |
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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("dkorat/bge-small-en-v1.5_setfit-sst2-english") |
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# Run inference |
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preds = model("a noble failure .") |
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``` |
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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 | 2 | 19.591 | 46 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 479 | |
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| 1 | 521 | |
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### Training Hyperparameters |
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- batch_size: (16, 2) |
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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: 1 |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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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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| 0.008 | 1 | 0.241 | - | |
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| 0.4 | 50 | 0.2525 | - | |
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| 0.8 | 100 | 0.0607 | - | |
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### Framework Versions |
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- Python: 3.10.13 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.3.0 |
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- Transformers: 4.37.2 |
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- PyTorch: 2.1.2+cu121 |
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- Datasets: 2.16.1 |
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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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