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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: Who was Cleopatra? She was a queen of ancient Egypt. |
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- text: Did you go anywhere interesting this weekend? Yes, I went to the zoo. |
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- text: Can robots think like humans? Not exactly, but AI can mimic some thinking |
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processes. |
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- text: Can you name an adjective? 'Quick' is an adjective because it describes. |
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- text: How does the water cycle work? Water evaporates, condenses into clouds, and |
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then precipitates back to the ground. |
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pipeline_tag: text-classification |
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inference: true |
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base_model: BAAI/bge-small-en-v1.5 |
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--- |
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# SetFit with BAAI/bge-small-en-v1.5 |
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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 [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |
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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:** 7 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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| English | <ul><li>"Can you tell me about your favorite book? I love 'Harry Potter' because it's full of magic and adventure."</li><li>'What did you learn about poems today? We learned about rhymes and how they create a rhythm in poems.'</li><li>"Can you make a sentence using the word 'enigmatic'? The old man's smile was enigmatic, making me wonder what secrets he hid."</li></ul> | |
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| Math | <ul><li>"What is 8 times 9? It's 72."</li><li>'How do you find the area of a rectangle? Multiply the length by the width.'</li><li>"What's the difference between a prime number and a composite number? A prime number has only two factors, 1 and itself, while a composite number has more than two factors."</li></ul> | |
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| Art | <ul><li>'What colors do you mix to make green? Yellow and blue make green.'</li><li>'Who painted the Mona Lisa? Leonardo da Vinci painted it.'</li><li>"What's the difference between sculpture and pottery? Sculpture is the art of making figures while pottery is specifically making vessels from clay."</li></ul> | |
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| Science | <ul><li>"What is photosynthesis? It's the process by which plants make their food using sunlight."</li><li>'Can you name the planets in our solar system? Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune.'</li><li>"What's the difference between a solid and a liquid? A solid has a fixed shape while a liquid takes the shape of its container."</li></ul> | |
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| History | <ul><li>'Who was the first president of the United States? George Washington was the first president.'</li><li>'Can you tell me about the Egyptian pyramids? They were massive tombs built for pharaohs, the biggest is the Pyramid of Giza.'</li><li>'What was the Renaissance? It was a period of great cultural and scientific advancement in Europe.'</li></ul> | |
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| Technology | <ul><li>"What is the Internet? It's a global network of computers that can share information."</li><li>'Can you name a famous computer scientist? Alan Turing is known as one of the fathers of computer science.'</li><li>"What does 'AI' stand for? It stands for Artificial Intelligence."</li></ul> | |
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| NONE | <ul><li>'What did you have for lunch today? I had a sandwich and some fruit.'</li><li>'Do you like playing outside? Yes, I love playing soccer with my friends.'</li><li>"What's your favorite TV show? I love watching 'SpongeBob SquarePants'."</li></ul> | |
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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("bew/setfit-subject-model-basic") |
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# Run inference |
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preds = model("Who was Cleopatra? She was a queen of ancient Egypt.") |
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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 | 6 | 14.1333 | 30 | |
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| Label | Training Sample Count | |
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|:-----------|:----------------------| |
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| Art | 10 | |
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| English | 10 | |
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| History | 10 | |
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| Math | 10 | |
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| NONE | 15 | |
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| Science | 10 | |
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| Technology | 10 | |
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### Training Hyperparameters |
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- batch_size: (32, 32) |
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- num_epochs: (10, 10) |
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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: 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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| 0.0067 | 1 | 0.1987 | - | |
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| 0.3333 | 50 | 0.1814 | - | |
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| 0.6667 | 100 | 0.128 | - | |
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| 1.0 | 150 | 0.0146 | - | |
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| 1.3333 | 200 | 0.006 | - | |
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| 1.6667 | 250 | 0.0037 | - | |
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| 2.0 | 300 | 0.0031 | - | |
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| 2.3333 | 350 | 0.0027 | - | |
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| 2.6667 | 400 | 0.0024 | - | |
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| 3.0 | 450 | 0.0024 | - | |
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| 3.3333 | 500 | 0.002 | - | |
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| 3.6667 | 550 | 0.002 | - | |
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| 4.0 | 600 | 0.0017 | - | |
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| 4.3333 | 650 | 0.0019 | - | |
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| 4.6667 | 700 | 0.0018 | - | |
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| 5.0 | 750 | 0.0014 | - | |
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| 5.3333 | 800 | 0.0013 | - | |
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| 5.6667 | 850 | 0.0014 | - | |
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| 6.0 | 900 | 0.0014 | - | |
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| 6.3333 | 950 | 0.0014 | - | |
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| 6.6667 | 1000 | 0.0016 | - | |
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| 7.0 | 1050 | 0.0013 | - | |
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| 7.3333 | 1100 | 0.0013 | - | |
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| 7.6667 | 1150 | 0.0012 | - | |
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| 8.0 | 1200 | 0.0014 | - | |
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| 8.3333 | 1250 | 0.001 | - | |
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| 8.6667 | 1300 | 0.0012 | - | |
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| 9.0 | 1350 | 0.0014 | - | |
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| 9.3333 | 1400 | 0.0012 | - | |
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| 9.6667 | 1450 | 0.0012 | - | |
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| 10.0 | 1500 | 0.0011 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.3.1 |
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- Transformers: 4.35.2 |
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- PyTorch: 2.1.0+cu121 |
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- Datasets: 2.17.0 |
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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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