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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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- accuracy |
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widget: |
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- text: The best thing about this is it drowned out the call from the guy angry cause |
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he hadn't gotten a tracking number... http://t.co/QYu8grOrQ1 |
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- text: 'http://t.co/a0v1ybySOD Its the best time of day!! åÊ @Siren_Voice is #liveonstreamate!' |
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- text: 16yr old PKK suicide bomber who detonated bomb in Turkey Army trench released |
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http://t.co/mMkLapX2ok |
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- text: '#hot Reddit''s new content policy goes into effect many horrible subreddits |
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banned or quarantined http://t.co/HqdCZzdmbN #prebreak #best' |
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- text: Heat wave warning aa? Ayyo dei. Just when I plan to visit friends after a |
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year. |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/all-mpnet-base-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/all-mpnet-base-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: accuracy |
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value: 0.8098990736900318 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/all-mpnet-base-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-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:** 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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### 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>'peanut butter cookie dough blizzard is ??????????????????????'</li><li>'Free Ebay Sniping RT? http://t.co/B231Ul1O1K Lumbar Extender Back Stretcher Excellent Condition!! ?Please Favorite & Share'</li><li>"'13 M. Chapoutier Crozes Hermitage so much purple violets slate crushed gravel white pepper. Yum #france #wine #DC http://t.co/skvWN38HZ7"</li></ul> | |
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| 1 | <ul><li>'DUST IN THE WIND: @82ndABNDIV paratroopers move to a loading zone during a dust storm in support of Operation Fury: http://t.co/uGesKLCn8M'</li><li>'Delhi Government to Provide Free Treatment to Acid Attack Victims in Private Hospitals http://t.co/H6PM1W7elL'</li><li>'National Briefing | West: California: Spring Oil Spill Estimate Grows: Documents released on Wednesday disclos... http://t.co/wBi7Laq18E'</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** | 0.8099 | |
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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("pEpOo/catastrophy") |
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# Run inference |
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preds = model("Heat wave warning aa? Ayyo dei. Just when I plan to visit friends after a year.") |
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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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### Out-of-Scope Use |
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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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### 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 | 2 | 15.3737 | 31 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 222 | |
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| 1 | 158 | |
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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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- num_iterations: 20 |
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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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| 0.0005 | 1 | 0.3038 | - | |
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| 0.0263 | 50 | 0.1867 | - | |
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| 0.0526 | 100 | 0.2578 | - | |
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| 0.0789 | 150 | 0.2298 | - | |
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| 0.1053 | 200 | 0.1253 | - | |
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| 0.1316 | 250 | 0.0446 | - | |
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| 0.1579 | 300 | 0.1624 | - | |
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| 0.1842 | 350 | 0.0028 | - | |
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| 0.2105 | 400 | 0.0059 | - | |
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| 0.2368 | 450 | 0.0006 | - | |
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| 0.2632 | 500 | 0.0287 | - | |
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| 0.2895 | 550 | 0.003 | - | |
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| 0.3158 | 600 | 0.0004 | - | |
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| 0.3421 | 650 | 0.0014 | - | |
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| 0.3684 | 700 | 0.0002 | - | |
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| 0.3947 | 750 | 0.0001 | - | |
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| 0.4211 | 800 | 0.0002 | - | |
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| 0.4474 | 850 | 0.0002 | - | |
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| 0.4737 | 900 | 0.0002 | - | |
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| 0.5 | 950 | 0.0826 | - | |
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| 0.5263 | 1000 | 0.0002 | - | |
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| 0.5526 | 1050 | 0.0001 | - | |
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| 0.5789 | 1100 | 0.0003 | - | |
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| 0.6053 | 1150 | 0.0303 | - | |
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| 0.6316 | 1200 | 0.0001 | - | |
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| 0.6579 | 1250 | 0.0 | - | |
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| 0.6842 | 1300 | 0.0001 | - | |
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| 0.7105 | 1350 | 0.0 | - | |
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| 0.7368 | 1400 | 0.0001 | - | |
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| 0.7632 | 1450 | 0.0002 | - | |
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| 0.7895 | 1500 | 0.0434 | - | |
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| 0.8158 | 1550 | 0.0001 | - | |
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| 0.8421 | 1600 | 0.0 | - | |
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| 0.8684 | 1650 | 0.0001 | - | |
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| 0.8947 | 1700 | 0.0001 | - | |
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| 0.9211 | 1750 | 0.0001 | - | |
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| 0.9474 | 1800 | 0.0001 | - | |
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| 0.9737 | 1850 | 0.0001 | - | |
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| 1.0 | 1900 | 0.0 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.1 |
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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: 2.15.0 |
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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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