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--- |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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library_name: setfit |
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metrics: |
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- f1 |
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pipeline_tag: text-classification |
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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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widget: |
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- text: I'm so excited for the weekend, I get to spend time with my friends and family. |
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We're planning a hike and then having a BBQ. I love days like this! |
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- text: You're just a stupid white person, you'll never understand what it's like |
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to be a minority. You're so privileged, you have no idea how much racism you've |
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experienced in your life. Get out of here with your entitled attitude. |
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- text: Are you f***ing kidding me?! This is the worst customer service I've ever |
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experienced. I've been on hold for 45 minutes and no one has even bothered to |
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answer my call. Unbelievable. |
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- text: You're such a f***ing idiot, how dare you even try to tell me what to do. |
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I swear to god, you're the most annoying person I've ever met. Just f*** off and |
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leave me alone. |
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- text: 'Just got the cutest puppy and I''m so in love with him! He''s already stolen |
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my heart and I''m sure he''ll bring so much joy to our family. Anyone else have |
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a furry friend at home? #puppylove #dogsofinstagram #loveofmylife' |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-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: f1 |
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value: 0.8648435963013968 |
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name: F1 |
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--- |
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# SetFit with sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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:** 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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### 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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**This dataset may contain racism, sexuality, or other undesired content.** |
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| Label | Examples | |
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|:----------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| Toxic | <ul><li>"Can't believe this black guy thinks he can be president. What a joke. He's only in office because of affirmative action. Get over it, America!"</li><li>"I'm so sick of you, you think you're so much better than everyone else just because you have a few more followers. Newsflash, you're just as toxic as the rest of us. Go cry to your mom."</li><li>"I'm going to smash your car window if you don't give me money right now. You're going to regret messing with me."</li></ul> | |
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| Non toxic | <ul><li>"I just wanted to say thank you to the amazing team at @CompanyX for their incredible customer service. They went above and beyond to resolve my issue and I'm so grateful for their help. #CustomerLove #Respect"</li><li>"I'm having a great day, thanks for asking! How about you? I just got back from a lovely hike in the mountains and I'm feeling refreshed."</li><li>"I'm feeling really overwhelmed with my coursework. Do you have any tips on how to manage my time effectively?"</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** | 0.8648 | |
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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("setfit_model_id") |
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# Run inference |
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preds = model("I'm so excited for the weekend, I get to spend time with my friends and family. We're planning a hike and then having a BBQ. I love days like this!") |
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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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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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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 | 14 | 27.5 | 42 | |
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| Label | Training Sample Count | |
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|:----------|:----------------------| |
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| Non toxic | 12 | |
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| Toxic | 20 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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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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- 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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| 0.0278 | 1 | 0.2873 | - | |
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| 1.0 | 36 | - | 0.1098 | |
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| 1.3889 | 50 | 0.0013 | - | |
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| **2.0** | **72** | **-** | **0.0981** | |
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| 2.7778 | 100 | 0.0003 | - | |
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| 3.0 | 108 | - | 0.112 | |
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| 4.0 | 144 | - | 0.1174 | |
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| 4.1667 | 150 | 0.0001 | - | |
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| 5.0 | 180 | - | 0.1075 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.9.19 |
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- SetFit: 1.1.0.dev0 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.4.0 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.15.2 |
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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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