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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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- precision |
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- recall |
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- f1 |
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widget: |
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- text: 'VALID THRU 02/10/2015 ' |
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- text: Les chaines Universal+ |
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- text: "\tminute = second * 60," |
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- text: ': Session' |
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- text: Phil Klay |
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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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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: accuracy |
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value: 0.87 |
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name: Accuracy |
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- type: precision |
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value: 0.8452380952380952 |
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name: Precision |
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- type: recall |
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value: 0.8452380952380952 |
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name: Recall |
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- type: f1 |
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value: 0.8452380952380952 |
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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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| Label | Examples | |
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|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------| |
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| True | <ul><li>'ace} UNIV FEATURING CHAMILLIONAIRE ©) '</li><li>'ee — Pra yon t “pink you are S© Mer '</li><li>'ae PTAA Be hs B corms of Rermee '</li></ul> | |
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| False | <ul><li>'mmmmmmmmmmlli'</li><li>'Manage Cookie Preferences'</li><li>'Supply Partners'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | Precision | Recall | F1 | |
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|:--------|:---------|:----------|:-------|:-------| |
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| **all** | 0.87 | 0.8452 | 0.8452 | 0.8452 | |
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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(": Session") |
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``` |
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### Out-of-Scope Use |
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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 | 1 | 8.4575 | 265 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| False | 384 | |
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| True | 416 | |
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### Training Hyperparameters |
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- batch_size: (16, 2) |
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- num_epochs: (1, 16) |
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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, 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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- run_name: PG-OCR-test-2 |
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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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| 0.0005 | 1 | 0.4077 | - | |
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| 0.025 | 50 | 0.2689 | - | |
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| 0.05 | 100 | 0.2505 | - | |
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| 0.075 | 150 | 0.1787 | - | |
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| 0.1 | 200 | 0.1602 | - | |
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| 0.125 | 250 | 0.1381 | - | |
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| 0.15 | 300 | 0.1753 | - | |
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| 0.175 | 350 | 0.018 | - | |
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| 0.2 | 400 | 0.024 | - | |
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| 0.225 | 450 | 0.0863 | - | |
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| 0.25 | 500 | 0.0504 | - | |
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| 0.275 | 550 | 0.0204 | - | |
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| 0.3 | 600 | 0.0124 | - | |
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| 0.325 | 650 | 0.066 | - | |
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| 0.35 | 700 | 0.1305 | - | |
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| 0.375 | 750 | 0.0599 | - | |
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| 0.4 | 800 | 0.0323 | - | |
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| 0.425 | 850 | 0.0039 | - | |
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| 0.45 | 900 | 0.0131 | - | |
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| 0.475 | 950 | 0.004 | - | |
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| 0.5 | 1000 | 0.0016 | - | |
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| 0.525 | 1050 | 0.0139 | - | |
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| 0.55 | 1100 | 0.0189 | - | |
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| 0.575 | 1150 | 0.0533 | - | |
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| 0.6 | 1200 | 0.0645 | - | |
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| 0.625 | 1250 | 0.0005 | - | |
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| 0.65 | 1300 | 0.0111 | - | |
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| 0.675 | 1350 | 0.002 | - | |
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| 0.7 | 1400 | 0.0082 | - | |
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| 0.725 | 1450 | 0.0009 | - | |
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| 0.75 | 1500 | 0.0018 | - | |
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| 0.775 | 1550 | 0.0003 | - | |
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| 0.8 | 1600 | 0.0108 | - | |
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| 0.825 | 1650 | 0.0009 | - | |
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| 0.85 | 1700 | 0.0003 | - | |
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| 0.875 | 1750 | 0.0009 | - | |
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| 0.9 | 1800 | 0.0038 | - | |
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| 0.925 | 1850 | 0.0406 | - | |
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| 0.95 | 1900 | 0.0012 | - | |
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| 0.975 | 1950 | 0.0024 | - | |
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| 1.0 | 2000 | 0.0004 | - | |
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### Framework Versions |
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- Python: 3.11.0 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.3.0 |
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- Transformers: 4.37.2 |
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- PyTorch: 2.2.1+cu121 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.15.1 |
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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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