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--- |
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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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widget: |
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- text: Write a 1000-word essay on the history of the Roman Empire. |
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- text: Tell a two-sentence horror story involving a smart fridge. |
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- text: Compare the economic policies of Keynesianism and Monetarism in 250 words. |
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- text: Explain the difference between HTTP and HTTPS |
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- text: Write a SQL stored procedure to handle GDPR data deletion requests |
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metrics: |
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- f1 |
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pipeline_tag: text-classification |
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library_name: setfit |
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inference: true |
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base_model: sentence-transformers/paraphrase-MiniLM-L6-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-MiniLM-L6-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: f1 |
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value: 1.0 |
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name: F1 |
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--- |
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# SetFit with sentence-transformers/paraphrase-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-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:** 128 tokens |
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- **Number of Classes:** 3 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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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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### Model Labels |
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| Label | Examples | |
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|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 1 | <ul><li>'Draft a polite email to reschedule a meeting'</li><li>"Translate the phrase 'The quick brown fox jumps over the lazy dog' into Mandarin, French, and German."</li><li>'Write a short story (under 200 words) about a robot discovering nature.'</li></ul> | |
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| 0 | <ul><li>'Analyze the ethical implications of CRISPR gene editing'</li><li>'Debug this Python multiprocessing code deadlock: [code snippet]'</li><li>'Implement a Python async websocket client with error handling'</li></ul> | |
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| 2 | <ul><li>"Python syntax to print 'Hello World'"</li><li>'What is 2+2?'</li><li>'Who is the current president of the United States?'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | F1 | |
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|:--------|:----| |
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| **all** | 1.0 | |
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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("setfit_model_id") |
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# Run inference |
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preds = model("Explain the difference between HTTP and HTTPS") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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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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--> |
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<!-- |
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### Recommendations |
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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 | 3 | 8.9583 | 17 | |
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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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| 2 | 8 | |
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### Training Hyperparameters |
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- batch_size: (8, 8) |
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- num_epochs: (3, 3) |
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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: (1e-05, 1e-05) |
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- head_learning_rate: 0.001 |
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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: True |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- max_length: 384 |
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- seed: 42 |
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- evaluation_strategy: epoch |
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- eval_max_steps: -1 |
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- load_best_model_at_end: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0083 | 1 | 0.4046 | - | |
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| 0.4167 | 50 | 0.2913 | - | |
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| 0.8333 | 100 | 0.1724 | - | |
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| 1.0 | 120 | - | 0.1897 | |
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| 1.25 | 150 | 0.0825 | - | |
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| 1.6667 | 200 | 0.0284 | - | |
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| 2.0 | 240 | - | 0.1806 | |
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| 2.0833 | 250 | 0.0137 | - | |
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| 2.5 | 300 | 0.0089 | - | |
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| 2.9167 | 350 | 0.007 | - | |
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| 3.0 | 360 | - | 0.1806 | |
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### Framework Versions |
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- Python: 3.11.13 |
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- SetFit: 1.1.2 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.53.3 |
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- PyTorch: 2.7.1 |
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- Datasets: 3.0.0 |
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- Tokenizers: 0.21.2 |
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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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