| --- |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| - generated_from_setfit_trainer |
| widget: |
| - text: We think rain is reducing, rain reducing, so we need to do something about |
| Russell. |
| - text: And it's still a long stint to the end, many laps. |
| - text: Jacob will box soon, we believe, and has been asked to remove tire management. |
| - text: Don't go too crazy on the brake warm up. |
| - text: Degradation seems low on the time. Emetal started off in a 38.8 and is now |
| on a 39.1 after 16 laps. |
| metrics: |
| - accuracy |
| pipeline_tag: text-classification |
| library_name: setfit |
| inference: true |
| --- |
| |
| # SetFit |
|
|
| 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. |
|
|
| 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:** [Unknown](https://huggingface.co/unknown) --> |
| - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
| - **Maximum Sequence Length:** 512 tokens |
| - **Number of Classes:** 5 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 | |
| |:------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | INFORMATION | <ul><li>'We lose a lot of time on the streets, easy laptop.'</li><li>'Okay, Lucid Gap 1.2, nice work. Right, eight laps to go once you cross the line.'</li><li>"Well done, Daniel. That's the checkered flag. P7, buddy. P7 is a good start. Well defended at the end. That was great driving last few laps. Cheers, guys. Good start. Obviously, I'll learn a bit from today. We'll keep getting better, but not a bad first weekend. Congrats."</li></ul> | |
| | PROBLEM | <ul><li>'I think I have still some damage. Balance is quite a bit off. Understood. We do see from data.'</li><li>'So that floor damage looks like it picked up at turn 8, so right hand side.'</li><li>'Okay, so switch off the engine please Lewis.'</li></ul> | |
| | ORDER | <ul><li>'Okay, mode race and focus on turn two preparation, turn two and three for this lap.'</li><li>'Okay, that last lap, same pace as signs. And mode seven. Mode seven.'</li><li>'Yuki, we are rear left limited, so watch we spin and we can push in the high speed. Push in the high speed.'</li></ul> | |
| | WARNING | <ul><li>'Be careful, a lot of people in the pit lane, so obviously they will move, but watch yourself as well.'</li><li>"The only concern I have, if the safety car comes out and we have to pit for that hard tyre and I can't get any temperature into it, we're in trouble. Yeah, copy Lewis, we're not concerned, we think everyone will be in the same boat, if not worse."</li><li>"So, Max, for info, you've been given a 10 second penalty for forcing Lando off track at turn 4. So, head down. 10. That's quite impressive. That was a lot of whinging. A lot."</li></ul> | |
| | QUESTION | <ul><li>"Do you want to flap adjust Nico? Not really, but it's new sticky tires. Let's maybe try. Which way would you go? I would take off, yeah. I would try less, I guess, and see what it feels like. Okay, copy. Take like half percent down. Turn 10 feels pretty low in grip, like tailwind, I think. The rear is pretty unhappy there. Yes, on the tailwind into 10. Also, 1 and 6 and 7 are tailwind. Okay, so we've taken off 0.5. We'll get a feel. One more grid. Go to the grid after this. Close the radio rear, it's too loud."</li><li>"That's it, mate. You are world champion. World champion. What a mate. I'm so proud. Lando, this is Zach from McLaren. Is this the world champion hotline? Yeah. You did it! You did it! Arthur! Woo! Thank you, guys. Oh my god. You made a kid's dream come true. Thank you so much. I love you guys. Thanks for everything. You deserve it. I love you, Mum. I love you, Dad. Thanks for everything. I'm not crying."</li><li>"And Lando, do you think you can get past? Otherwise, what about plan B? Remember R switch, R switch. Yeah, if you've got the goblins, go for it."</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("setfit_model_id") |
| # Run inference |
| preds = model("Don't go too crazy on the brake warm up.") |
| ``` |
|
|
| <!-- |
| ### 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 | 2 | 27.2063 | 386 | |
|
|
| | Label | Training Sample Count | |
| |:------------|:----------------------| |
| | INFORMATION | 32 | |
| | PROBLEM | 32 | |
| | ORDER | 32 | |
| | WARNING | 32 | |
| | QUESTION | 32 | |
|
|
| ### 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 | |
| |:------:|:----:|:-------------:|:---------------:| |
| | 0.0013 | 1 | 0.1755 | - | |
| | 0.0625 | 50 | 0.3393 | - | |
| | 0.125 | 100 | 0.3649 | - | |
| | 0.1875 | 150 | 0.4159 | - | |
| | 0.25 | 200 | 0.3289 | - | |
| | 0.3125 | 250 | 0.3171 | - | |
| | 0.375 | 300 | 0.2952 | - | |
| | 0.4375 | 350 | 0.3004 | - | |
| | 0.5 | 400 | 0.3111 | - | |
| | 0.5625 | 450 | 0.3039 | - | |
| | 0.625 | 500 | 0.2702 | - | |
| | 0.6875 | 550 | 0.2891 | - | |
| | 0.75 | 600 | 0.2793 | - | |
| | 0.8125 | 650 | 0.2652 | - | |
| | 0.875 | 700 | 0.2761 | - | |
| | 0.9375 | 750 | 0.2603 | - | |
| | 1.0 | 800 | 0.2543 | 0.5 | |
|
|
| ### Framework Versions |
| - Python: 3.12.13 |
| - SetFit: 1.1.3 |
| - Sentence Transformers: 5.4.1 |
| - Transformers: 5.0.0 |
| - PyTorch: 2.10.0+cu128 |
| - Datasets: 5.0.0 |
| - Tokenizers: 0.22.2 |
|
|
| ## 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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