| --- |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| - generated_from_setfit_trainer |
| widget: |
| - text: 'Trump stated he wanted to stockpile 1% of all BTC. ' |
| - text: about what nigga |
| - text: hehe panicked yield chaser exiting |
| - text: Haarland should win the only reason he doesnt is because his international |
| team is shit |
| - text: Lol prove it |
| metrics: |
| - name: Macro F1 |
| type: f1_macro |
| value: 0.6928 |
| - name: Accuracy |
| type: accuracy |
| value: 0.7607 |
| - name: F1 NOISE |
| type: f1_noise |
| value: 0.8366 |
| - name: Precision NOISE |
| type: precision_noise |
| value: 0.8421 |
| - name: Recall NOISE |
| type: recall_noise |
| value: 0.8312 |
| - name: F1 META |
| type: f1_meta |
| value: 0.5238 |
| - name: Precision META |
| type: precision_meta |
| value: 0.5 |
| - name: Recall META |
| type: recall_meta |
| value: 0.55 |
| - name: F1 SUBSTANTIVE |
| type: f1_substantive |
| value: 0.7179 |
| - name: Precision SUBSTANTIVE |
| type: precision_substantive |
| value: 0.7368 |
| - name: Recall SUBSTANTIVE |
| type: recall_substantive |
| value: 0.7 |
| pipeline_tag: text-classification |
| library_name: setfit |
| inference: true |
| base_model: sentence-transformers/all-mpnet-base-v2 |
| --- |
| |
| # SetFit with sentence-transformers/all-mpnet-base-v2 |
|
|
| 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. |
|
|
| The model has been trained using an efficient few-shot learning technique that involves: |
|
|
| 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
| 2. Training a classification head with features from the fine-tuned Sentence Transformer. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** SetFit |
| - **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) |
| - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
| - **Maximum Sequence Length:** 384 tokens |
| - **Number of Classes:** 3 classes |
| <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
| - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
| - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
|
|
| ### Model Labels |
| | Label | Examples | |
| |:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | 0 | <ul><li>'maybe coffeezilla was right that Polymarket is just a tool for insiders to make money....'</li><li>'Coping yes kids in chat '</li><li>'The most common with ABOP'</li></ul> | |
| | 1 | <ul><li>'Right now, I am just waiting for the shares for march 31st to go back down to 26-31c so I can buy more shares with what I won so far'</li><li>'If I am insider, I would do the opposite, scoop up yes quietly and not tell influencers. By the time you see it tweeter you are the liquidity'</li><li>'Looks like Syrian government bet NO in this market :) https://kyivindependent.com/russias-evacuation-efforts-stalled-as-new-syrian-leaders-deny-port-access-media-reports/'</li></ul> | |
| | 2 | <ul><li>'Somewhere in GOP 1-64 is where I think it\'ll end up. Feel good about PA, GA, and NC. Normally Trump underpolls big time in Wisconsin, but recent statewide elections there don\'t look good for Trump. Michigan was a fluke in 2016, when Dems are focused that state is so hard to flip. AZ is full of McCain & Flake "cuck" Republicans so don\'t feel good there about Trump\'s chances, NV is a better flip opportunity in the Southwest imho.'</li><li>'According to multiple sources, President Joe Biden signed a bill to avoid a government shutdown on December 20, 2024. This action was reported across various news outlets and social media: The Senate passed a stopgap funding bill shortly after the midnight funding deadline, and the House had passed it earlier that evening. President Biden was set to sign this legislation, which would extend government funding into March and include provisions for disaster relief and farm aid. Posts on X also confirmed that the bill was passed by Congress and sent to President Biden for signing, with the explicit mention that it averted a shutdown. These sources collectively indicate that the signing took place on December 20, 2024.'</li><li>"Looks like he's about to do it https://apnews.com/article/trump-deportees-el-salvador-contempt-boasberg-da282511ac6f5c8dd19af620995ca440"</li></ul> | |
|
|
| ## Uses |
|
|
| ### Direct Use for Inference |
|
|
| First install the SetFit library: |
|
|
| ```bash |
| pip install setfit |
| ``` |
|
|
| Then you can load this model and run inference. |
|
|
| ```python |
| from setfit import SetFitModel |
| |
| # Download from the 🤗 Hub |
| model = SetFitModel.from_pretrained("gehaustein/PolyQual-3") |
| # Run inference |
| preds = model("Lol prove it") |
| ``` |
|
|
| <!-- |
| ### Downstream Use |
|
|
| *List how someone could finetune this model on their own dataset.* |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Set Metrics |
| | Training set | Min | Median | Max | |
| |:-------------|:----|:--------|:----| |
| | Word count | 1 | 22.3420 | 199 | |
|
|
| | Label | Training Sample Count | |
| |:------|:----------------------| |
| | 0 | 307 | |
| | 1 | 307 | |
| | 2 | 307 | |
|
|
| ### Training Hyperparameters |
| - batch_size: (8, 8) |
| - num_epochs: (1, 1) |
| - max_steps: -1 |
| - sampling_strategy: oversampling |
| - num_iterations: 20 |
| - body_learning_rate: (2e-05, 1e-05) |
| - head_learning_rate: 0.01 |
| - loss: CosineSimilarityLoss |
| - distance_metric: cosine_distance |
| - margin: 0.25 |
| - end_to_end: False |
| - use_amp: False |
| - warmup_proportion: 0.1 |
| - l2_weight: 0.01 |
| - seed: 42 |
| - eval_max_steps: -1 |
| - load_best_model_at_end: True |
|
|
| ### Training Results |
| | Epoch | Step | Training Loss | Validation Loss | |
| |:------:|:----:|:-------------:|:---------------:| |
| | 0.0002 | 1 | 0.6034 | - | |
| | 0.0109 | 50 | 0.3441 | 0.3746 | |
| | 0.0217 | 100 | 0.3198 | 0.3002 | |
| | 0.0326 | 150 | 0.2498 | 0.2823 | |
| | 0.0434 | 200 | 0.2468 | 0.2755 | |
| | 0.0543 | 250 | 0.2242 | 0.2678 | |
| | 0.0651 | 300 | 0.174 | 0.2492 | |
| | 0.0760 | 350 | 0.1182 | 0.2157 | |
| | 0.0869 | 400 | 0.0824 | 0.2100 | |
| | 0.0977 | 450 | 0.0433 | 0.2346 | |
| | 0.1086 | 500 | 0.0248 | 0.2168 | |
| | 0.1194 | 550 | 0.0183 | 0.2211 | |
|
|
| ### Framework Versions |
| - Python: 3.12.13 |
| - SetFit: 1.1.3 |
| - Sentence Transformers: 5.3.0 |
| - Transformers: 4.49.0 |
| - PyTorch: 2.10.0+cu128 |
| - Datasets: 4.0.0 |
| - Tokenizers: 0.21.4 |
|
|
| ## Citation |
|
|
| ### BibTeX |
| ```bibtex |
| @article{https://doi.org/10.48550/arxiv.2209.11055, |
| doi = {10.48550/ARXIV.2209.11055}, |
| url = {https://arxiv.org/abs/2209.11055}, |
| author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
| keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
| title = {Efficient Few-Shot Learning Without Prompts}, |
| publisher = {arXiv}, |
| year = {2022}, |
| copyright = {Creative Commons Attribution 4.0 International} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
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| <!-- |
| ## Model Card Authors |
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|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
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| ## Model Card Contact |
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| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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