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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: The DOJ surrendered in the matter and accepted the fact that the plans are |
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protected under the First Amendment. |
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- text: The settlement was approved by a federal judge. |
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- text: The project in question is under the banner of the Vox-Pol Network of Excellence, |
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which “is designed to comprehensively research, analyse, debate, and critique |
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issues surrounding violent online political extremism (VOPE).” |
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- text: And the more substantive those debates become, the further away the US discourse |
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will move from the mendacious assumptions of Obama’s Iran policy – that the Iranian |
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regime is a responsible actor and potential US ally, and that there is nothing |
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inherently aggressive or problematic about Iran’s illicit nuclear weapons program. |
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- text: Of course, the UN has reached no such conclusion. |
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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: 0.4317180616740088 |
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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 [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:** [Unknown](https://huggingface.co/unknown) --> |
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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:** 512 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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| 0.0 | <ul><li>'Pamela Geller and Robert Spencer co-founded anti-Muslim group Stop Islamization of America.\n'</li><li>'He added: "We condemn all those whose behaviours and views run counter to our shared values and will not stand for extremism in any form."\n'</li><li>'Ms Geller, of the Atlas Shrugs blog, and Mr Spencer, of Jihad Watch, are also co-founders of the American Freedom Defense Initiative, best known for a pro-Israel "Defeat Jihad" poster campaign on the New York subway.\n'</li></ul> | |
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| 1.0 | <ul><li>'On both of their blogs the pair called their bans from entering the UK "a striking blow against freedom" and said the "the nation that gave the world the Magna Carta is dead".\n'</li><li>'A researcher with the organisation, Matthew Collins, said it was "delighted" with the decision.\n'</li><li>'Lead attorney Matt Gonzalez has argued that the weapon was a SIG Sauer with a "hair trigger in single-action mode" — a model well-known for accidental discharges even among experienced shooters.\n'</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** | 0.4317 | |
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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("anismahmahi/improve-G3-setfit-model") |
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# Run inference |
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preds = model("The settlement was approved by a federal judge.") |
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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 | 26.2226 | 129 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 2362 | |
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| 1 | 1784 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (2, 2) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 5 |
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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: 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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| 0.0004 | 1 | 0.3949 | - | |
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| 0.0193 | 50 | 0.2806 | - | |
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| 0.0386 | 100 | 0.2461 | - | |
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| 0.0579 | 150 | 0.2522 | - | |
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| 0.0772 | 200 | 0.279 | - | |
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| 0.0965 | 250 | 0.2149 | - | |
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| 0.1157 | 300 | 0.2513 | - | |
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| 0.1350 | 350 | 0.2426 | - | |
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| 0.1543 | 400 | 0.2696 | - | |
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| 0.1736 | 450 | 0.2485 | - | |
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| 0.1929 | 500 | 0.2209 | - | |
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| 0.2122 | 550 | 0.2412 | - | |
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| 0.2315 | 600 | 0.1801 | - | |
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| 0.2508 | 650 | 0.197 | - | |
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| 0.2701 | 700 | 0.2223 | - | |
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| 0.2894 | 750 | 0.1825 | - | |
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| 0.3086 | 800 | 0.2067 | - | |
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| 0.3279 | 850 | 0.1726 | - | |
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| 0.3472 | 900 | 0.2091 | - | |
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| 0.3665 | 950 | 0.2159 | - | |
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| 0.3858 | 1000 | 0.2433 | - | |
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| 0.4051 | 1050 | 0.1102 | - | |
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| 0.4244 | 1100 | 0.081 | - | |
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| 0.4437 | 1150 | 0.1661 | - | |
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| 0.4630 | 1200 | 0.1574 | - | |
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| 0.4823 | 1250 | 0.1458 | - | |
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| 0.5015 | 1300 | 0.0881 | - | |
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| 0.5208 | 1350 | 0.0683 | - | |
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| 0.5401 | 1400 | 0.2053 | - | |
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| 0.5594 | 1450 | 0.0581 | - | |
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| 0.5787 | 1500 | 0.0742 | - | |
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| 0.5980 | 1550 | 0.1775 | - | |
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| 0.6173 | 1600 | 0.0541 | - | |
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| 0.6366 | 1650 | 0.1086 | - | |
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| 0.6559 | 1700 | 0.0654 | - | |
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| 0.6752 | 1750 | 0.0909 | - | |
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| 0.6944 | 1800 | 0.0571 | - | |
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| 0.7137 | 1850 | 0.0016 | - | |
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| 0.7330 | 1900 | 0.0963 | - | |
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| 0.7523 | 1950 | 0.0063 | - | |
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| 0.7716 | 2000 | 0.0011 | - | |
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| 0.7909 | 2050 | 0.0033 | - | |
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| 0.8102 | 2100 | 0.0069 | - | |
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| 0.8295 | 2150 | 0.0013 | - | |
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| 0.8488 | 2200 | 0.0051 | - | |
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| 0.8681 | 2250 | 0.0596 | - | |
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| 0.8873 | 2300 | 0.0007 | - | |
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| 0.9066 | 2350 | 0.0122 | - | |
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| 0.9259 | 2400 | 0.0012 | - | |
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| 0.9452 | 2450 | 0.0003 | - | |
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| 0.9645 | 2500 | 0.0012 | - | |
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| 0.9838 | 2550 | 0.002 | - | |
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| **1.0** | **2592** | **-** | **0.2706** | |
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| 1.0031 | 2600 | 0.001 | - | |
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| 1.0224 | 2650 | 0.0015 | - | |
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| 1.0417 | 2700 | 0.0594 | - | |
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| 1.0610 | 2750 | 0.0011 | - | |
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| 1.0802 | 2800 | 0.0087 | - | |
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| 1.0995 | 2850 | 0.0608 | - | |
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| 1.1188 | 2900 | 0.0531 | - | |
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| 1.1381 | 2950 | 0.0006 | - | |
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| 1.1574 | 3000 | 0.001 | - | |
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| 1.1767 | 3050 | 0.06 | - | |
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| 1.1960 | 3100 | 0.0003 | - | |
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| 1.2153 | 3150 | 0.0004 | - | |
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| 1.2346 | 3200 | 0.0002 | - | |
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| 1.2539 | 3250 | 0.0007 | - | |
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| 1.2731 | 3300 | 0.0006 | - | |
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| 1.2924 | 3350 | 0.0005 | - | |
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| 1.3117 | 3400 | 0.0007 | - | |
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| 1.3310 | 3450 | 0.0001 | - | |
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| 1.3503 | 3500 | 0.0587 | - | |
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| 1.3696 | 3550 | 0.0002 | - | |
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| 1.3889 | 3600 | 0.0001 | - | |
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| 1.4082 | 3650 | 0.0003 | - | |
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| 1.4275 | 3700 | 0.0002 | - | |
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| 1.4468 | 3750 | 0.0011 | - | |
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| 1.4660 | 3800 | 0.0007 | - | |
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| 1.4853 | 3850 | 0.0001 | - | |
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| 1.5046 | 3900 | 0.0001 | - | |
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| 1.5239 | 3950 | 0.0002 | - | |
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| 1.5432 | 4000 | 0.0001 | - | |
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| 1.5625 | 4050 | 0.0003 | - | |
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| 1.5818 | 4100 | 0.0002 | - | |
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| 1.6011 | 4150 | 0.0001 | - | |
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| 1.6204 | 4200 | 0.0002 | - | |
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| 1.6397 | 4250 | 0.0002 | - | |
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| 1.6590 | 4300 | 0.0003 | - | |
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| 1.6782 | 4350 | 0.0003 | - | |
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| 1.6975 | 4400 | 0.0002 | - | |
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| 1.7168 | 4450 | 0.0001 | - | |
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| 1.7361 | 4500 | 0.0037 | - | |
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| 1.7554 | 4550 | 0.0002 | - | |
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| 1.7747 | 4600 | 0.0001 | - | |
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| 1.7940 | 4650 | 0.0001 | - | |
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| 1.8133 | 4700 | 0.0001 | - | |
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| 1.8326 | 4750 | 0.0001 | - | |
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| 1.8519 | 4800 | 0.0003 | - | |
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| 1.8711 | 4850 | 0.0002 | - | |
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| 1.8904 | 4900 | 0.0001 | - | |
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| 1.9097 | 4950 | 0.0004 | - | |
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| 1.9290 | 5000 | 0.0001 | - | |
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| 1.9483 | 5050 | 0.0001 | - | |
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| 1.9676 | 5100 | 0.0001 | - | |
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| 1.9869 | 5150 | 0.0004 | - | |
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| 2.0 | 5184 | - | 0.2802 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.2 |
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- Sentence Transformers: 2.2.2 |
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- Transformers: 4.35.2 |
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- PyTorch: 2.1.0+cu121 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.15.0 |
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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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