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--- |
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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: Tennengebirge Reef |
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- text: Outcrop next to I-84 East |
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- text: scenic overview |
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- text: Ruby Star for sale now Please contact us for more details. Regards |
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- text: torre rocosa de grans dimensions. 3 blocs partits |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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library_name: setfit |
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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:** 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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| 0 | <ul><li>'Calcite'</li><li>'biotite. Contact metamorphosis'</li><li>'rail trail'</li></ul> | |
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| 1 | <ul><li>'Geafitti on tree and burn scar on ground'</li><li>'another beautiful rock from the same place'</li><li>'Vhfgv'</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("setfit_model_id") |
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# Run inference |
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preds = model("scenic overview") |
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``` |
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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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<!-- |
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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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<!-- |
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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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<!-- |
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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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--> |
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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 | 7.2788 | 1899 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 2997 | |
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| 1 | 783 | |
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### Training Hyperparameters |
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- batch_size: (32, 32) |
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- num_epochs: (1, 1) |
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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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- l2_weight: 0.01 |
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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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| 0.0002 | 1 | 0.2331 | - | |
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| 0.0106 | 50 | 0.2391 | - | |
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| 0.0212 | 100 | 0.238 | - | |
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| 0.0317 | 150 | 0.2309 | - | |
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| 0.0423 | 200 | 0.2117 | - | |
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| 0.0529 | 250 | 0.1879 | - | |
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| 0.0635 | 300 | 0.1745 | - | |
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| 0.0741 | 350 | 0.1708 | - | |
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| 0.0847 | 400 | 0.1402 | - | |
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| 0.0952 | 450 | 0.1349 | - | |
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| 0.1058 | 500 | 0.1092 | - | |
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| 0.1164 | 550 | 0.1031 | - | |
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| 0.1270 | 600 | 0.0828 | - | |
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| 0.1376 | 650 | 0.0756 | - | |
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| 0.1481 | 700 | 0.0587 | - | |
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| 0.1587 | 750 | 0.0487 | - | |
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| 0.1693 | 800 | 0.0557 | - | |
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| 0.1799 | 850 | 0.0456 | - | |
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| 0.1905 | 900 | 0.0371 | - | |
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| 0.2011 | 950 | 0.0412 | - | |
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| 0.2116 | 1000 | 0.0382 | - | |
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| 0.2222 | 1050 | 0.0376 | - | |
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| 0.2328 | 1100 | 0.0353 | - | |
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| 0.2434 | 1150 | 0.0346 | - | |
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| 0.2540 | 1200 | 0.0364 | - | |
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| 0.2646 | 1250 | 0.0317 | - | |
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| 0.2751 | 1300 | 0.0374 | - | |
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| 0.2857 | 1350 | 0.0282 | - | |
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| 0.2963 | 1400 | 0.0255 | - | |
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| 0.3069 | 1450 | 0.023 | - | |
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| 0.3175 | 1500 | 0.0287 | - | |
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| 0.3280 | 1550 | 0.025 | - | |
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| 0.3386 | 1600 | 0.0216 | - | |
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| 0.3492 | 1650 | 0.0241 | - | |
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| 0.3598 | 1700 | 0.0234 | - | |
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| 0.3704 | 1750 | 0.0279 | - | |
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| 0.3810 | 1800 | 0.0239 | - | |
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| 0.3915 | 1850 | 0.0199 | - | |
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| 0.4021 | 1900 | 0.0252 | - | |
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| 0.4127 | 1950 | 0.0219 | - | |
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| 0.4233 | 2000 | 0.0228 | - | |
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| 0.4339 | 2050 | 0.0204 | - | |
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| 0.4444 | 2100 | 0.0231 | - | |
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| 0.4550 | 2150 | 0.0144 | - | |
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| 0.4656 | 2200 | 0.0229 | - | |
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| 0.4762 | 2250 | 0.0129 | - | |
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| 0.4868 | 2300 | 0.0219 | - | |
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| 0.4974 | 2350 | 0.0194 | - | |
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| 0.5079 | 2400 | 0.0172 | - | |
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| 0.5185 | 2450 | 0.0177 | - | |
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| 0.5291 | 2500 | 0.0252 | - | |
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| 0.5397 | 2550 | 0.0251 | - | |
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| 0.5503 | 2600 | 0.014 | - | |
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| 0.5608 | 2650 | 0.0204 | - | |
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| 0.5714 | 2700 | 0.0248 | - | |
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| 0.5820 | 2750 | 0.0146 | - | |
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| 0.5926 | 2800 | 0.0191 | - | |
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| 0.6032 | 2850 | 0.0223 | - | |
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| 0.6138 | 2900 | 0.0206 | - | |
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| 0.6243 | 2950 | 0.0163 | - | |
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| 0.6349 | 3000 | 0.0235 | - | |
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| 0.6455 | 3050 | 0.0245 | - | |
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| 0.6561 | 3100 | 0.0199 | - | |
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| 0.6667 | 3150 | 0.0145 | - | |
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| 0.6772 | 3200 | 0.016 | - | |
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| 0.6878 | 3250 | 0.0143 | - | |
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| 0.6984 | 3300 | 0.0206 | - | |
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| 0.7090 | 3350 | 0.0187 | - | |
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| 0.7196 | 3400 | 0.0168 | - | |
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| 0.7302 | 3450 | 0.0148 | - | |
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| 0.7407 | 3500 | 0.0212 | - | |
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| 0.7513 | 3550 | 0.0185 | - | |
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| 0.7619 | 3600 | 0.015 | - | |
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| 0.7725 | 3650 | 0.0187 | - | |
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| 0.7831 | 3700 | 0.0161 | - | |
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| 0.7937 | 3750 | 0.0204 | - | |
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| 0.8042 | 3800 | 0.0182 | - | |
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| 0.8148 | 3850 | 0.0157 | - | |
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| 0.8254 | 3900 | 0.0197 | - | |
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| 0.8360 | 3950 | 0.0133 | - | |
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| 0.8466 | 4000 | 0.0211 | - | |
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| 0.8571 | 4050 | 0.0155 | - | |
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| 0.8677 | 4100 | 0.0197 | - | |
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| 0.8783 | 4150 | 0.0168 | - | |
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| 0.8889 | 4200 | 0.0139 | - | |
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| 0.8995 | 4250 | 0.0132 | - | |
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| 0.9101 | 4300 | 0.018 | - | |
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| 0.9206 | 4350 | 0.014 | - | |
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| 0.9312 | 4400 | 0.017 | - | |
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| 0.9418 | 4450 | 0.0173 | - | |
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| 0.9524 | 4500 | 0.0163 | - | |
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| 0.9630 | 4550 | 0.0178 | - | |
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| 0.9735 | 4600 | 0.0176 | - | |
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| 0.9841 | 4650 | 0.0126 | - | |
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| 0.9947 | 4700 | 0.0194 | - | |
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### Framework Versions |
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- Python: 3.12.9 |
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- SetFit: 1.1.2 |
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- Sentence Transformers: 4.1.0 |
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- Transformers: 4.52.4 |
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- PyTorch: 2.7.1 |
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- Datasets: 3.6.0 |
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- Tokenizers: 0.21.1 |
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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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<!-- |
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*Clearly define terms in order to be accessible across audiences.* |
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