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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 development of smart cities is leveraging technology to improve urban |
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living conditions. |
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- text: Climate change is causing a significant rise in sea levels. |
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- text: Fans are speculating about the plot of the upcoming season of Stranger Things. |
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- text: Fashion branding and marketing campaigns shape consumer perceptions and influence |
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purchasing decisions. |
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- text: Volunteering abroad provides a unique opportunity to experience different |
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cultures while giving back to society. |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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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:** 12 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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| Politics | <ul><li>'The mayor announced a new initiative to improve public transportation.'</li><li>'The senator is facing criticism for her stance on the recent bill.'</li><li>'The upcoming election has sparked intense debates among the candidates.'</li></ul> | |
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| Health | <ul><li>'Regular exercise and a balanced diet are key to maintaining good health.'</li><li>'The World Health Organization has issued new guidelines on COVID-19.'</li><li>'A new study reveals the benefits of meditation for mental health.'</li></ul> | |
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| Finance | <ul><li>'The stock market saw a significant drop following the announcement.'</li><li>'Investing in real estate can be a profitable venture if done correctly.'</li><li>"The company's profits have doubled since the launch of their new product."</li></ul> | |
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| Travel | <ul><li>'Visiting the Grand Canyon is a breathtaking experience.'</li><li>'The tourism industry has been severely impacted by the pandemic.'</li><li>'Backpacking through Europe is a popular choice for young travelers.'</li></ul> | |
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| Food | <ul><li>'The new restaurant in town offers a fusion of Italian and Japanese cuisine.'</li><li>'Drinking eight glasses of water a day is essential for staying hydrated.'</li><li>'Cooking classes are a fun way to learn new recipes and techniques.'</li></ul> | |
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| Education | <ul><li>'The school district is implementing a new curriculum for the upcoming year.'</li><li>'Online learning has become increasingly popular during the pandemic.'</li><li>'The university is offering scholarships for students in financial need.'</li></ul> | |
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| Environment | <ul><li>'Climate change is causing a significant rise in sea levels.'</li><li>'Recycling and composting are effective ways to reduce waste.'</li><li>'The Amazon rainforest is home to millions of unique species.'</li></ul> | |
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| Fashion | <ul><li>'The new fashion trend is all about sustainability and eco-friendly materials.'</li><li>'The annual Met Gala is a major event in the fashion world.'</li><li>'Vintage clothing has made a comeback in recent years.'</li></ul> | |
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| Science | <ul><li>"NASA's Mars Rover has made significant discoveries about the red planet."</li><li>'The Nobel Prize in Physics was awarded for breakthroughs in black hole research.'</li><li>'Genetic engineering is opening up new possibilities in medical treatment.'</li></ul> | |
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| Sports | <ul><li>'The NBA Finals are set to begin next week with the top two teams in the league.'</li><li>'Serena Williams continues to dominate the tennis world with her powerful serve.'</li><li>'The World Cup is the most prestigious tournament in international soccer.'</li></ul> | |
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| Technology | <ul><li>'Artificial intelligence is changing the way we live and work.'</li><li>'The latest iPhone has a number of exciting new features.'</li><li>'Cybersecurity is becoming increasingly important as more and more data moves online.'</li></ul> | |
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| Entertainment | <ul><li>'The new Marvel movie is breaking box office records.'</li><li>'The Grammy Awards are a celebration of the best music of the year.'</li><li>'The latest season of Game of Thrones had fans on the edge of their seats.'</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("EmeraldMP/ANLP_kaggle") |
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# Run inference |
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preds = model("Climate change is causing a significant rise in sea levels.") |
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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 | 7 | 12.8073 | 24 | |
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| Label | Training Sample Count | |
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|:--------------|:----------------------| |
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| Education | 23 | |
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| Entertainment | 23 | |
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| Environment | 23 | |
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| Fashion | 23 | |
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| Finance | 23 | |
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| Food | 23 | |
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| Health | 23 | |
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| Politics | 22 | |
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| Science | 23 | |
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| Sports | 23 | |
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| Technology | 23 | |
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| Travel | 23 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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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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- 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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| 0.0015 | 1 | 0.2748 | - | |
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| 0.0727 | 50 | 0.2537 | - | |
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| 0.1453 | 100 | 0.1734 | - | |
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| 0.2180 | 150 | 0.1086 | - | |
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| 0.2907 | 200 | 0.062 | - | |
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| 0.3634 | 250 | 0.046 | - | |
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| 0.4360 | 300 | 0.017 | - | |
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| 0.5087 | 350 | 0.0104 | - | |
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| 0.5814 | 400 | 0.006 | - | |
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| 0.6541 | 450 | 0.0021 | - | |
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| 0.7267 | 500 | 0.0052 | - | |
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| 0.7994 | 550 | 0.0045 | - | |
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| 0.8721 | 600 | 0.0012 | - | |
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| 0.9448 | 650 | 0.0007 | - | |
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| 1.0174 | 700 | 0.0006 | - | |
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| 1.0901 | 750 | 0.0006 | - | |
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| 1.1628 | 800 | 0.0006 | - | |
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| 1.2355 | 850 | 0.0005 | - | |
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| 1.3081 | 900 | 0.0004 | - | |
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| 1.3808 | 950 | 0.0003 | - | |
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| 1.4535 | 1000 | 0.0004 | - | |
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| 1.5262 | 1050 | 0.0004 | - | |
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| 1.5988 | 1100 | 0.0004 | - | |
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| 1.6715 | 1150 | 0.0003 | - | |
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| 1.7442 | 1200 | 0.0002 | - | |
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| 1.8169 | 1250 | 0.0002 | - | |
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| 1.8895 | 1300 | 0.0005 | - | |
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| 1.9622 | 1350 | 0.0004 | - | |
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| 2.0349 | 1400 | 0.0002 | - | |
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| 2.1076 | 1450 | 0.0004 | - | |
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| 2.1802 | 1500 | 0.0002 | - | |
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| 2.2529 | 1550 | 0.0002 | - | |
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| 2.3256 | 1600 | 0.0004 | - | |
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| 2.3983 | 1650 | 0.0002 | - | |
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| 2.4709 | 1700 | 0.0002 | - | |
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| 2.5436 | 1750 | 0.0002 | - | |
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| 2.6163 | 1800 | 0.0002 | - | |
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| 2.6890 | 1850 | 0.0002 | - | |
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| 2.7616 | 1900 | 0.0003 | - | |
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| 2.8343 | 1950 | 0.0001 | - | |
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| 2.9070 | 2000 | 0.0002 | - | |
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| 2.9797 | 2050 | 0.0002 | - | |
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| 3.0523 | 2100 | 0.0003 | - | |
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| 3.125 | 2150 | 0.0002 | - | |
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| 3.1977 | 2200 | 0.0002 | - | |
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| 3.2703 | 2250 | 0.0001 | - | |
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| 3.3430 | 2300 | 0.0002 | - | |
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| 3.4157 | 2350 | 0.0002 | - | |
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| 3.4884 | 2400 | 0.0002 | - | |
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| 3.5610 | 2450 | 0.0001 | - | |
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| 3.6337 | 2500 | 0.0001 | - | |
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| 3.7064 | 2550 | 0.0001 | - | |
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| 3.7791 | 2600 | 0.0001 | - | |
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| 3.8517 | 2650 | 0.0001 | - | |
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| 3.9244 | 2700 | 0.0001 | - | |
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| 3.9971 | 2750 | 0.0001 | - | |
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| 4.0698 | 2800 | 0.0001 | - | |
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| 4.1424 | 2850 | 0.0001 | - | |
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| 4.2151 | 2900 | 0.0001 | - | |
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| 4.2878 | 2950 | 0.0001 | - | |
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| 4.3605 | 3000 | 0.0001 | - | |
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| 4.4331 | 3050 | 0.0001 | - | |
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| 4.5058 | 3100 | 0.0001 | - | |
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| 4.5785 | 3150 | 0.0001 | - | |
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| 4.6512 | 3200 | 0.0001 | - | |
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| 4.7238 | 3250 | 0.0001 | - | |
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| 4.7965 | 3300 | 0.0001 | - | |
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| 4.8692 | 3350 | 0.0001 | - | |
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| 4.9419 | 3400 | 0.0001 | - | |
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| 5.0145 | 3450 | 0.0001 | - | |
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| 5.0872 | 3500 | 0.0001 | - | |
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| 5.1599 | 3550 | 0.0001 | - | |
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| 5.2326 | 3600 | 0.0001 | - | |
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| 5.3052 | 3650 | 0.0001 | - | |
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| 5.3779 | 3700 | 0.0001 | - | |
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| 5.4506 | 3750 | 0.0001 | - | |
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| 5.5233 | 3800 | 0.0001 | - | |
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| 5.5959 | 3850 | 0.0001 | - | |
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| 5.6686 | 3900 | 0.0001 | - | |
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| 5.7413 | 3950 | 0.0001 | - | |
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| 5.8140 | 4000 | 0.0001 | - | |
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| 5.8866 | 4050 | 0.0001 | - | |
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| 5.9593 | 4100 | 0.0001 | - | |
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| 6.0320 | 4150 | 0.0001 | - | |
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| 6.1047 | 4200 | 0.0001 | - | |
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| 6.1773 | 4250 | 0.0001 | - | |
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| 6.25 | 4300 | 0.0001 | - | |
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| 6.3227 | 4350 | 0.0001 | - | |
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| 6.3953 | 4400 | 0.0001 | - | |
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| 6.4680 | 4450 | 0.0001 | - | |
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| 6.5407 | 4500 | 0.0001 | - | |
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| 6.6134 | 4550 | 0.0001 | - | |
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| 6.6860 | 4600 | 0.0001 | - | |
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| 6.7587 | 4650 | 0.0001 | - | |
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| 6.8314 | 4700 | 0.0001 | - | |
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| 6.9041 | 4750 | 0.0001 | - | |
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| 6.9767 | 4800 | 0.0 | - | |
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| 7.0494 | 4850 | 0.0001 | - | |
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| 7.1221 | 4900 | 0.0001 | - | |
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| 7.1948 | 4950 | 0.0001 | - | |
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| 7.2674 | 5000 | 0.0001 | - | |
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| 7.3401 | 5050 | 0.0001 | - | |
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| 7.4128 | 5100 | 0.0001 | - | |
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| 7.4855 | 5150 | 0.0001 | - | |
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| 7.5581 | 5200 | 0.0001 | - | |
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| 7.6308 | 5250 | 0.0001 | - | |
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| 7.7035 | 5300 | 0.0001 | - | |
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| 7.7762 | 5350 | 0.0001 | - | |
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| 7.8488 | 5400 | 0.0001 | - | |
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| 7.9215 | 5450 | 0.0001 | - | |
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| 7.9942 | 5500 | 0.0 | - | |
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| 8.0669 | 5550 | 0.0001 | - | |
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| 8.1395 | 5600 | 0.0001 | - | |
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| 8.2122 | 5650 | 0.0001 | - | |
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| 8.2849 | 5700 | 0.0 | - | |
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| 8.3576 | 5750 | 0.0001 | - | |
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| 8.4302 | 5800 | 0.0001 | - | |
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| 8.5029 | 5850 | 0.0001 | - | |
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| 8.5756 | 5900 | 0.0001 | - | |
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| 8.6483 | 5950 | 0.0001 | - | |
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| 8.7209 | 6000 | 0.0001 | - | |
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| 8.7936 | 6050 | 0.0001 | - | |
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| 8.8663 | 6100 | 0.0 | - | |
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| 8.9390 | 6150 | 0.0 | - | |
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| 9.0116 | 6200 | 0.0001 | - | |
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| 9.0843 | 6250 | 0.0001 | - | |
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| 9.1570 | 6300 | 0.0 | - | |
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| 9.2297 | 6350 | 0.0 | - | |
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| 9.3023 | 6400 | 0.0 | - | |
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| 9.375 | 6450 | 0.0001 | - | |
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| 9.4477 | 6500 | 0.0001 | - | |
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| 9.5203 | 6550 | 0.0001 | - | |
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| 9.5930 | 6600 | 0.0001 | - | |
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| 9.6657 | 6650 | 0.0001 | - | |
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| 9.7384 | 6700 | 0.0001 | - | |
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| 9.8110 | 6750 | 0.0001 | - | |
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| 9.8837 | 6800 | 0.0001 | - | |
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| 9.9564 | 6850 | 0.0 | - | |
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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: 2.7.0 |
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- Transformers: 4.38.2 |
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- PyTorch: 2.2.1+cu121 |
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- Datasets: 2.18.0 |
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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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