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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: >- |
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Quelles sont les règles en matière de garde d'enfants et de pension |
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alimentaire ? |
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- text: Comment se déroule une procédure de divorce ? |
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- text: >- |
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Quelles sont les principales difficultés rencontrées dans l'application de |
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cette loi ? |
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- text: Quels sont les régimes matrimoniaux possibles ? |
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- text: >- |
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Comment peut-on obtenir réparation pour un préjudice subi du fait d'une |
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décision administrative illégale ? |
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pipeline_tag: text-classification |
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inference: true |
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base_model: intfloat/multilingual-e5-small |
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model-index: |
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- name: SetFit with intfloat/multilingual-e5-small |
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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: 1 |
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name: Accuracy |
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language: |
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- fr |
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- en |
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--- |
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# SetFit with intfloat/multilingual-e5-small |
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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 [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) |
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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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<!-- - **Language:** Unknown --> |
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<!-- - **License:** 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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| independent | <ul><li>'Comment rédiger un contrat de travail ?'</li><li>'Quels sont les impôts et taxes applicables aux entreprises ?'</li><li>'Comment peut-on contester un licenciement abusif ?'</li></ul> | |
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| follow_up | <ul><li>'Quelles sont les conséquences de cette loi ?'</li><li>"Comment cette loi s'inscrit-elle dans le cadre plus large du droit algérien ?"</li><li>"Comment puis-je obtenir plus d'informations sur ce sujet ?"</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 1.0 | |
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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("super-cinnamon/fewshot-followup-multi-e5") |
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# Run inference |
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preds = model("Comment se déroule une procédure de divorce ?") |
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``` |
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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 | 9.6184 | 16 | |
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| Label | Training Sample Count | |
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|:------------|:----------------------| |
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| independent | 43 | |
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| follow_up | 33 | |
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### Training Hyperparameters |
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- batch_size: (8, 8) |
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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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| 0.0027 | 1 | 0.3915 | - | |
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| 0.1326 | 50 | 0.3193 | - | |
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| 0.2653 | 100 | 0.2252 | - | |
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| 0.3979 | 150 | 0.1141 | - | |
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| 0.5305 | 200 | 0.0197 | - | |
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| 0.6631 | 250 | 0.0019 | - | |
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| 0.7958 | 300 | 0.0021 | - | |
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| 0.9284 | 350 | 0.0002 | - | |
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| 1.0610 | 400 | 0.0008 | - | |
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| 1.1936 | 450 | 0.0005 | - | |
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| 1.3263 | 500 | 0.0002 | - | |
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| 1.4589 | 550 | 0.0002 | - | |
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| 1.5915 | 600 | 0.0007 | - | |
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| 1.7241 | 650 | 0.0001 | - | |
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| 1.8568 | 700 | 0.0003 | - | |
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| 1.9894 | 750 | 0.0002 | - | |
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| 2.1220 | 800 | 0.0001 | - | |
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| 2.2546 | 850 | 0.0002 | - | |
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| 2.3873 | 900 | 0.0 | - | |
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| 2.5199 | 950 | 0.0003 | - | |
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| 2.6525 | 1000 | 0.0001 | - | |
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| 2.7851 | 1050 | 0.0001 | - | |
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| 2.9178 | 1100 | 0.0001 | - | |
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| 3.0504 | 1150 | 0.0001 | - | |
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| 3.1830 | 1200 | 0.0001 | - | |
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| 3.3156 | 1250 | 0.0001 | - | |
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| 3.4483 | 1300 | 0.0001 | - | |
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| 3.5809 | 1350 | 0.0001 | - | |
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| 3.7135 | 1400 | 0.0 | - | |
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| 3.8462 | 1450 | 0.0 | - | |
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| 3.9788 | 1500 | 0.0 | - | |
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| 4.1114 | 1550 | 0.0 | - | |
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| 4.2440 | 1600 | 0.0001 | - | |
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| 4.3767 | 1650 | 0.0001 | - | |
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| 4.5093 | 1700 | 0.0001 | - | |
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| 4.6419 | 1750 | 0.0001 | - | |
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| 4.7745 | 1800 | 0.0 | - | |
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| 4.9072 | 1850 | 0.0001 | - | |
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| 5.0398 | 1900 | 0.0 | - | |
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| 5.1724 | 1950 | 0.0001 | - | |
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| 5.3050 | 2000 | 0.0 | - | |
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| 5.4377 | 2050 | 0.0001 | - | |
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| 5.5703 | 2100 | 0.0 | - | |
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| 5.7029 | 2150 | 0.0 | - | |
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| 5.8355 | 2200 | 0.0 | - | |
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| 5.9682 | 2250 | 0.0001 | - | |
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| 6.1008 | 2300 | 0.0001 | - | |
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| 6.2334 | 2350 | 0.0 | - | |
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| 6.3660 | 2400 | 0.0001 | - | |
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| 6.4987 | 2450 | 0.0 | - | |
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| 6.6313 | 2500 | 0.0 | - | |
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| 6.7639 | 2550 | 0.0 | - | |
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| 6.8966 | 2600 | 0.0 | - | |
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| 7.0292 | 2650 | 0.0 | - | |
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| 7.1618 | 2700 | 0.0 | - | |
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| 7.2944 | 2750 | 0.0 | - | |
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| 7.4271 | 2800 | 0.0001 | - | |
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| 7.5597 | 2850 | 0.0 | - | |
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| 7.6923 | 2900 | 0.0 | - | |
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| 7.8249 | 2950 | 0.0 | - | |
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| 7.9576 | 3000 | 0.0 | - | |
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| 8.0902 | 3050 | 0.0 | - | |
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| 8.2228 | 3100 | 0.0 | - | |
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| 8.3554 | 3150 | 0.0 | - | |
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| 8.4881 | 3200 | 0.0001 | - | |
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| 8.6207 | 3250 | 0.0 | - | |
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| 8.7533 | 3300 | 0.0 | - | |
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| 8.8859 | 3350 | 0.0 | - | |
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| 9.0186 | 3400 | 0.0001 | - | |
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| 9.1512 | 3450 | 0.0 | - | |
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| 9.2838 | 3500 | 0.0 | - | |
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| 9.4164 | 3550 | 0.0001 | - | |
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| 9.5491 | 3600 | 0.0 | - | |
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| 9.6817 | 3650 | 0.0001 | - | |
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| 9.8143 | 3700 | 0.0 | - | |
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| 9.9469 | 3750 | 0.0001 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.1 |
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- Sentence Transformers: 2.2.2 |
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- Transformers: 4.35.2 |
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- PyTorch: 2.1.0+cu118 |
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- Datasets: 2.15.0 |
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- Tokenizers: 0.15.0 |
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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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