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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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- f1 |
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widget: |
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- text: What could possibly go wrong? |
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- text: We may have faith that human inventiveness will prevail in the long run. |
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- text: That can happen again. |
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- text: But in fact it was intensely rational. |
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- text: Chinese crime, like Chinese cuisine, varies according to regional origin. |
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pipeline_tag: text-classification |
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inference: true |
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model-index: |
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- name: SetFit |
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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: 0.7526132404181185 |
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name: F1 |
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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:** 256 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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| SUBJ | <ul><li>'Gone are the days when they led the world in recession-busting'</li><li>'Who so mean that he will not himself be taxed, who so mindful of wealth that he will not favor increasing the popular taxes, in aid of these defective children?'</li><li>'That state has sixty-two counties and sixty cities … In addition there are 932 towns, 507 villages, and, at the last count, 9,600 school districts … Just try to render efficient service … amid the diffused identities and inevitable jealousies of, roughly, 11,000 independent administrative officers or boards!'</li></ul> | |
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| OBJ | <ul><li>'Is this a warning of what’s to come?'</li><li>'This unique set of circumstances has brought PCL back into focus as the safe haven of choice for global players seeking somewhere to stash their cash.'</li><li>'Socialists believe that, if everyone cannot have something, no one shall.'</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** | 0.7526 | |
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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_subj_SVC") |
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# Run inference |
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preds = model("That can happen again.") |
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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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--> |
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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 | 35.9834 | 97 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| OBJ | 117 | |
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| SUBJ | 124 | |
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### Training Hyperparameters |
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- batch_size: (8, 8) |
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- num_epochs: (5, 5) |
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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: 1e-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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| 0.0008 | 1 | 0.3862 | - | |
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| 0.0415 | 50 | 0.4092 | - | |
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| 0.0830 | 100 | 0.3596 | - | |
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| 0.1245 | 150 | 0.2618 | - | |
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| 0.1660 | 200 | 0.2447 | - | |
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| 0.2075 | 250 | 0.263 | - | |
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| 0.2490 | 300 | 0.2583 | - | |
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| 0.2905 | 350 | 0.3336 | - | |
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| 0.3320 | 400 | 0.2381 | - | |
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| 0.3734 | 450 | 0.2454 | - | |
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| 0.4149 | 500 | 0.259 | - | |
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| 0.4564 | 550 | 0.2083 | - | |
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| 0.4979 | 600 | 0.2437 | - | |
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| 0.5394 | 650 | 0.2231 | - | |
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| 0.5809 | 700 | 0.0891 | - | |
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| 0.6224 | 750 | 0.1164 | - | |
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| 0.6639 | 800 | 0.0156 | - | |
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| 0.7054 | 850 | 0.0394 | - | |
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| 0.7469 | 900 | 0.0065 | - | |
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| 0.7884 | 950 | 0.0024 | - | |
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| 0.8299 | 1000 | 0.0012 | - | |
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| 0.8714 | 1050 | 0.0014 | - | |
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| 0.9129 | 1100 | 0.0039 | - | |
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| 0.9544 | 1150 | 0.0039 | - | |
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| 0.9959 | 1200 | 0.001 | - | |
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| 1.0373 | 1250 | 0.0007 | - | |
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| 1.0788 | 1300 | 0.0003 | - | |
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| 1.1203 | 1350 | 0.001 | - | |
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| 1.1618 | 1400 | 0.0003 | - | |
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| 1.2033 | 1450 | 0.0003 | - | |
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| 1.2448 | 1500 | 0.0014 | - | |
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| 1.2863 | 1550 | 0.0003 | - | |
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| 1.3278 | 1600 | 0.0003 | - | |
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| 1.3693 | 1650 | 0.0001 | - | |
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| 1.4108 | 1700 | 0.0004 | - | |
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| 1.4523 | 1750 | 0.0003 | - | |
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| 1.4938 | 1800 | 0.0008 | - | |
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| 1.5353 | 1850 | 0.0002 | - | |
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| 1.5768 | 1900 | 0.0005 | - | |
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| 1.6183 | 1950 | 0.0002 | - | |
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| 1.6598 | 2000 | 0.0004 | - | |
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| 1.7012 | 2050 | 0.0001 | - | |
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| 1.7427 | 2100 | 0.0002 | - | |
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| 1.7842 | 2150 | 0.0002 | - | |
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| 1.8257 | 2200 | 0.0002 | - | |
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| 1.8672 | 2250 | 0.0003 | - | |
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| 1.9087 | 2300 | 0.0001 | - | |
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| 1.9502 | 2350 | 0.0002 | - | |
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| 1.9917 | 2400 | 0.0001 | - | |
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| 2.0332 | 2450 | 0.0003 | - | |
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| 2.0747 | 2500 | 0.0002 | - | |
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| 2.1162 | 2550 | 0.0001 | - | |
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| 2.1577 | 2600 | 0.0001 | - | |
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| 2.1992 | 2650 | 0.0004 | - | |
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| 2.2407 | 2700 | 0.0002 | - | |
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| 2.2822 | 2750 | 0.0001 | - | |
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| 2.3237 | 2800 | 0.0005 | - | |
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| 2.3651 | 2850 | 0.0002 | - | |
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| 2.4066 | 2900 | 0.0003 | - | |
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| 2.4481 | 2950 | 0.0001 | - | |
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| 2.4896 | 3000 | 0.0001 | - | |
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| 2.5311 | 3050 | 0.0001 | - | |
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| 2.5726 | 3100 | 0.0001 | - | |
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| 2.6141 | 3150 | 0.0002 | - | |
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| 2.6556 | 3200 | 0.0001 | - | |
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| 2.6971 | 3250 | 0.0002 | - | |
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| 2.7386 | 3300 | 0.0002 | - | |
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| 2.7801 | 3350 | 0.0001 | - | |
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| 2.8216 | 3400 | 0.0001 | - | |
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| 2.8631 | 3450 | 0.0001 | - | |
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| 2.9046 | 3500 | 0.0001 | - | |
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| 2.9461 | 3550 | 0.0 | - | |
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| 2.9876 | 3600 | 0.0002 | - | |
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| 3.0290 | 3650 | 0.0001 | - | |
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| 3.0705 | 3700 | 0.0 | - | |
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| 3.1120 | 3750 | 0.0001 | - | |
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| 3.1535 | 3800 | 0.0001 | - | |
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| 3.1950 | 3850 | 0.0001 | - | |
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| 3.2365 | 3900 | 0.0001 | - | |
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| 3.2780 | 3950 | 0.0001 | - | |
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| 3.3195 | 4000 | 0.0001 | - | |
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| 3.3610 | 4050 | 0.0001 | - | |
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| 3.4025 | 4100 | 0.0 | - | |
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| 3.4440 | 4150 | 0.0001 | - | |
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| 3.4855 | 4200 | 0.0001 | - | |
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| 3.5270 | 4250 | 0.0001 | - | |
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| 3.5685 | 4300 | 0.0001 | - | |
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| 3.6100 | 4350 | 0.0002 | - | |
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| 3.6515 | 4400 | 0.0001 | - | |
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| 3.6929 | 4450 | 0.0001 | - | |
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| 3.7344 | 4500 | 0.0 | - | |
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| 3.7759 | 4550 | 0.0 | - | |
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| 3.8174 | 4600 | 0.0001 | - | |
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| 3.8589 | 4650 | 0.0001 | - | |
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| 3.9004 | 4700 | 0.0001 | - | |
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| 3.9419 | 4750 | 0.0 | - | |
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| 3.9834 | 4800 | 0.0001 | - | |
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| 4.0249 | 4850 | 0.0001 | - | |
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| 4.0664 | 4900 | 0.0001 | - | |
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| 4.1079 | 4950 | 0.0001 | - | |
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| 4.1494 | 5000 | 0.0 | - | |
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| 4.1909 | 5050 | 0.0 | - | |
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| 4.2324 | 5100 | 0.0 | - | |
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| 4.2739 | 5150 | 0.0 | - | |
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| 4.3154 | 5200 | 0.0001 | - | |
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| 4.3568 | 5250 | 0.0001 | - | |
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| 4.3983 | 5300 | 0.0001 | - | |
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| 4.4398 | 5350 | 0.0 | - | |
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| 4.4813 | 5400 | 0.0001 | - | |
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| 4.5228 | 5450 | 0.0 | - | |
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| 4.5643 | 5500 | 0.0001 | - | |
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| 4.6058 | 5550 | 0.0001 | - | |
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| 4.6473 | 5600 | 0.0001 | - | |
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| 4.6888 | 5650 | 0.0 | - | |
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| 4.7303 | 5700 | 0.0001 | - | |
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| 4.7718 | 5750 | 0.0001 | - | |
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| 4.8133 | 5800 | 0.0001 | - | |
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| 4.8548 | 5850 | 0.0 | - | |
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| 4.8963 | 5900 | 0.0 | - | |
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| 4.9378 | 5950 | 0.0 | - | |
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| 4.9793 | 6000 | 0.0001 | - | |
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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: 4.40.1 |
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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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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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