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--- |
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library_name: setfit |
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metrics: |
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- f1 |
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- accuracy |
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pipeline_tag: text-classification |
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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: To make introductions between Camelot's Chairman and the Cabinet Secretary. |
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We discussed the operation of the UK National Lottery and how to maximise returns |
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to National Lottery Good Causes as well as our plans to celebrate the 25th birthday |
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of The National Lottery. |
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- text: Discussion on crime |
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- text: To discuss Northern Powerhouse Rail and HS2 |
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- text: To discuss food security |
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- text: Electricity market |
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inference: false |
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model-index: |
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- name: SetFit |
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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: f1 |
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value: 0.9056603773584904 |
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name: F1 |
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- type: accuracy |
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value: 0.9572649572649573 |
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name: Accuracy |
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--- |
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# SetFit |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [Unknown](https://huggingface.co/unknown) --> |
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- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 4 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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## Evaluation |
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### Metrics |
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| Label | F1 | Accuracy | |
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|:--------|:-------|:---------| |
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| **all** | 0.9057 | 0.9573 | |
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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("twright8/setfit-oversample-labels-lobbying") |
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# Run inference |
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preds = model("Electricity market") |
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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 | 2 | 21.5644 | 153 | |
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### Training Hyperparameters |
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- batch_size: (16, 2) |
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- num_epochs: (6, 9) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (7.928034854554858e-06, 2.7001088851580374e-05) |
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- head_learning_rate: 0.009321171293151879 |
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- loss: CoSENTLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: True |
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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: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:-------:|:--------:|:-------------:|:---------------:| |
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| 0.0018 | 1 | 8.669 | - | |
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| 0.0880 | 50 | 8.6617 | - | |
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| 0.1761 | 100 | 12.5549 | - | |
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| 0.2641 | 150 | 3.1895 | - | |
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| 0.3521 | 200 | 16.3181 | - | |
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| 0.4401 | 250 | 0.7513 | - | |
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| 0.5282 | 300 | 4.6653 | - | |
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| 0.0018 | 1 | 0.0059 | - | |
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| 0.0880 | 50 | 3.4564 | - | |
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| 0.1761 | 100 | 0.5523 | - | |
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| 0.2641 | 150 | 0.2372 | - | |
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| 0.3521 | 200 | 4.288 | - | |
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| 0.4401 | 250 | 0.0027 | - | |
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| 0.5282 | 300 | 0.0002 | - | |
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| 0.6162 | 350 | 0.0002 | - | |
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| 0.7042 | 400 | 0.0001 | - | |
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| 0.7923 | 450 | 0.0015 | - | |
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| 0.8803 | 500 | 3.5596 | - | |
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| 0.9683 | 550 | 0.0 | - | |
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| 1.0 | 568 | - | 10.2261 | |
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| 1.0563 | 600 | 0.0 | - | |
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| 1.1444 | 650 | 0.0011 | - | |
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| 1.2324 | 700 | 0.0013 | - | |
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| 1.3204 | 750 | 0.0037 | - | |
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| 1.4085 | 800 | 0.0013 | - | |
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| 1.4965 | 850 | 0.0002 | - | |
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| 1.5845 | 900 | 0.0 | - | |
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| 1.6725 | 950 | 0.0 | - | |
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| 1.7606 | 1000 | 0.0001 | - | |
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| 1.8486 | 1050 | 0.0001 | - | |
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| 1.9366 | 1100 | 0.0001 | - | |
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| 2.0 | 1136 | - | 8.4908 | |
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| 2.0246 | 1150 | 0.0001 | - | |
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| 2.1127 | 1200 | 0.0 | - | |
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| 2.2007 | 1250 | 0.0005 | - | |
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| 2.2887 | 1300 | 0.0004 | - | |
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| 2.3768 | 1350 | 0.0 | - | |
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| 2.4648 | 1400 | 0.0009 | - | |
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| 2.5528 | 1450 | 0.0 | - | |
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| 2.6408 | 1500 | 0.0 | - | |
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| 2.7289 | 1550 | 0.0 | - | |
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| 2.8169 | 1600 | 0.0 | - | |
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| 2.9049 | 1650 | 0.0001 | - | |
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| 2.9930 | 1700 | 0.0003 | - | |
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| 3.0 | 1704 | - | 8.5594 | |
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| 3.0810 | 1750 | 0.0001 | - | |
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| 3.1690 | 1800 | 0.0 | - | |
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| 3.2570 | 1850 | 0.0002 | - | |
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| 3.3451 | 1900 | 0.0001 | - | |
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| 3.4331 | 1950 | 0.0 | - | |
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| 3.5211 | 2000 | 0.0 | - | |
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| 3.6092 | 2050 | 0.0 | - | |
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| 3.6972 | 2100 | 0.0 | - | |
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| 3.7852 | 2150 | 0.0 | - | |
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| 3.8732 | 2200 | 0.0002 | - | |
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| 3.9613 | 2250 | 0.0001 | - | |
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| **4.0** | **2272** | **-** | **8.4573** | |
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| 4.0493 | 2300 | 0.0 | - | |
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| 4.1373 | 2350 | 0.0 | - | |
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| 4.2254 | 2400 | 0.0002 | - | |
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| 4.3134 | 2450 | 0.0 | - | |
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| 4.4014 | 2500 | 0.0003 | - | |
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| 4.4894 | 2550 | 0.0001 | - | |
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| 4.5775 | 2600 | 0.0001 | - | |
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| 4.6655 | 2650 | 0.0001 | - | |
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| 4.7535 | 2700 | 0.0001 | - | |
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| 4.8415 | 2750 | 0.0001 | - | |
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| 4.9296 | 2800 | 0.0012 | - | |
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| 5.0 | 2840 | - | 8.6305 | |
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| 5.0176 | 2850 | 0.0009 | - | |
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| 5.1056 | 2900 | 0.0 | - | |
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| 5.1937 | 2950 | 0.0001 | - | |
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| 5.2817 | 3000 | 0.0 | - | |
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| 5.3697 | 3050 | 0.0 | - | |
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| 5.4577 | 3100 | 0.0001 | - | |
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| 5.5458 | 3150 | 0.0007 | - | |
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| 5.6338 | 3200 | 0.0002 | - | |
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| 5.7218 | 3250 | 0.0 | - | |
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| 5.8099 | 3300 | 0.0001 | - | |
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| 5.8979 | 3350 | 0.0002 | - | |
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| 5.9859 | 3400 | 0.0 | - | |
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| 6.0 | 3408 | - | 8.9528 | |
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* The bold row denotes the saved checkpoint. |
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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: 3.0.1 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.3.1+cu118 |
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- Datasets: 2.20.0 |
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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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