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--- |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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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: Now that the baffling, elongated, hyperreal coronation has occurred—no, not |
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that one—and Liz Truss has become Prime Minister, a degree of intervention and |
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action on energy bills has emerged, ahead of the looming socioeconomic catastrophe |
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facing the country this winter. |
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- text: But it needs to go much further. |
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- text: What could possibly go wrong? |
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- text: If you are White you might feel bad about hurting others or you might feel |
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afraid to lose this privilege….Overcoming White privilege is a job that must start |
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with the White community…. |
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- text: '[JF: Obviously, immigration wasn’t stopped: the current population of the |
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United States is 329.5 million—it passed 300 million in 2006.' |
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inference: true |
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--- |
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# SetFit |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A SVC 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:** [Unknown](https://huggingface.co/unknown) --> |
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- **Classification head:** a SVC instance |
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- **Maximum Sequence Length:** 384 tokens |
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- **Number of Classes:** 2 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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| 0 | <ul><li>'ESG funds often charge many times more for investment funds that are nearly indistinguishable from those without the ESG title.'</li><li>'They are California, Florida, Illinois, Nebraska, New York, and Wyoming.'</li><li>'And so it goes.'</li></ul> | |
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| 1 | <ul><li>'Republicans attempted to pass a resolution that would have enabled Congress to force workers to accept a deal, which was fortunately blocked by (who else) Senator Bernie Sanders.'</li><li>'No government ever surrenders power, even its emergency powers—not really.'</li><li>'No citizen in a democratic society should want executives from $10trn financial institutions to play a larger role than they already do in defining and implementing social values.'</li></ul> | |
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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("SOUMYADEEPSAR/Setfit_random_sample_svm_head") |
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# Run inference |
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preds = model("What could possibly go wrong?") |
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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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### 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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## 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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--> |
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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 | 23.4159 | 68 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 136 | |
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| 1 | 78 | |
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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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- 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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| 0.0003 | 1 | 0.3597 | - | |
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| 0.0161 | 50 | 0.2693 | - | |
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| 0.0323 | 100 | 0.2501 | - | |
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| 0.0484 | 150 | 0.2691 | - | |
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| 0.0645 | 200 | 0.063 | - | |
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| 0.0806 | 250 | 0.0179 | - | |
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| 0.0968 | 300 | 0.0044 | - | |
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| 0.1129 | 350 | 0.0003 | - | |
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| 0.1290 | 400 | 0.0005 | - | |
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| 0.1452 | 450 | 0.0002 | - | |
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| 0.1613 | 500 | 0.0003 | - | |
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| 0.1774 | 550 | 0.0001 | - | |
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| 0.1935 | 600 | 0.0001 | - | |
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| 0.2097 | 650 | 0.0001 | - | |
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| 0.2258 | 700 | 0.0001 | - | |
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| 0.2419 | 750 | 0.0001 | - | |
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| 0.2581 | 800 | 0.0 | - | |
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| 0.2742 | 850 | 0.0001 | - | |
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| 0.2903 | 900 | 0.0002 | - | |
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| 0.3065 | 950 | 0.0 | - | |
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| 0.3226 | 1000 | 0.0 | - | |
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| 0.3387 | 1050 | 0.0002 | - | |
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| 0.3548 | 1100 | 0.0 | - | |
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| 0.3710 | 1150 | 0.0001 | - | |
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| 0.3871 | 1200 | 0.0001 | - | |
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| 0.4032 | 1250 | 0.0 | - | |
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| 0.4194 | 1300 | 0.0 | - | |
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| 0.4355 | 1350 | 0.0 | - | |
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| 0.4516 | 1400 | 0.0001 | - | |
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| 0.4677 | 1450 | 0.0 | - | |
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| 0.4839 | 1500 | 0.0 | - | |
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| 0.5 | 1550 | 0.0001 | - | |
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| 0.5161 | 1600 | 0.0001 | - | |
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| 0.5323 | 1650 | 0.0 | - | |
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| 0.5484 | 1700 | 0.0 | - | |
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| 0.5645 | 1750 | 0.0 | - | |
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| 0.5806 | 1800 | 0.0 | - | |
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| 0.5968 | 1850 | 0.0 | - | |
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| 0.6129 | 1900 | 0.0 | - | |
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| 0.6290 | 1950 | 0.0001 | - | |
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| 0.6452 | 2000 | 0.0 | - | |
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| 0.6613 | 2050 | 0.0 | - | |
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| 0.6774 | 2100 | 0.0 | - | |
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| 0.6935 | 2150 | 0.0001 | - | |
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| 0.7097 | 2200 | 0.0 | - | |
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| 0.7258 | 2250 | 0.0 | - | |
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| 0.7419 | 2300 | 0.0001 | - | |
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| 0.7581 | 2350 | 0.0001 | - | |
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| 0.7742 | 2400 | 0.0001 | - | |
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| 0.7903 | 2450 | 0.0 | - | |
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| 0.8065 | 2500 | 0.0 | - | |
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| 0.8226 | 2550 | 0.0 | - | |
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| 0.8387 | 2600 | 0.0 | - | |
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| 0.8548 | 2650 | 0.0001 | - | |
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| 0.8710 | 2700 | 0.0001 | - | |
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| 0.8871 | 2750 | 0.0 | - | |
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| 0.9032 | 2800 | 0.0 | - | |
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| 0.9194 | 2850 | 0.0 | - | |
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| 0.9355 | 2900 | 0.0001 | - | |
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| 0.9516 | 2950 | 0.0 | - | |
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| 0.9677 | 3000 | 0.0001 | - | |
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| 0.9839 | 3050 | 0.0 | - | |
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| 1.0 | 3100 | 0.0 | - | |
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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: 3.0.1 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.3.0+cu121 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.15.2 |
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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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