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--- |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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library_name: setfit |
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metrics: |
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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: Enable audio recorder app |
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- text: Open video camera mode |
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- text: Show recent chats |
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- text: Switch to instant camera usage mode |
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- text: Could you switch to video camera mode? |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/all-MiniLM-L6-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: 1.0 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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:** 256 tokens |
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- **Number of Classes:** 3 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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| microphone | <ul><li>'Launch microphone app'</li><li>'Launch recording app'</li><li>'Access mic app'</li></ul> | |
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| history | <ul><li>'View chat logs'</li><li>'Display conversation details'</li><li>'Show history'</li></ul> | |
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| camera | <ul><li>'Switch to webcam mode please'</li><li>'Could you switch to video camera mode?'</li><li>'Open the photo webcam'</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("porxelek/word-classification") |
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# Run inference |
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preds = model("Show recent chats") |
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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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## 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 | 4.1364 | 10 | |
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| Label | Training Sample Count | |
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|:-----------|:----------------------| |
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| camera | 250 | |
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| history | 150 | |
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| microphone | 150 | |
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### Training Hyperparameters |
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- batch_size: (64, 64) |
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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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- 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: 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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| 0.0003 | 1 | 0.1209 | - | |
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| 0.0164 | 50 | 0.1449 | - | |
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| 0.0328 | 100 | 0.046 | - | |
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| 0.0492 | 150 | 0.0099 | - | |
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| 0.0656 | 200 | 0.0049 | - | |
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| 0.0820 | 250 | 0.0036 | - | |
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| 0.0985 | 300 | 0.0022 | - | |
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| 0.1149 | 350 | 0.0015 | - | |
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| 0.1313 | 400 | 0.0011 | - | |
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| 0.1477 | 450 | 0.001 | - | |
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| 0.1641 | 500 | 0.0009 | - | |
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| 0.1805 | 550 | 0.0009 | - | |
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| 0.1969 | 600 | 0.0009 | - | |
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| 0.2133 | 650 | 0.0008 | - | |
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| 0.2297 | 700 | 0.0007 | - | |
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| 0.2461 | 750 | 0.0006 | - | |
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| 0.2626 | 800 | 0.0006 | - | |
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| 0.2790 | 850 | 0.0006 | - | |
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| 0.2954 | 900 | 0.0006 | - | |
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| 0.3118 | 950 | 0.0005 | - | |
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| 0.3282 | 1000 | 0.0004 | - | |
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| 0.3446 | 1050 | 0.0005 | - | |
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| 0.3610 | 1100 | 0.0005 | - | |
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| 0.3774 | 1150 | 0.0004 | - | |
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| 0.3938 | 1200 | 0.0004 | - | |
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| 0.4102 | 1250 | 0.0004 | - | |
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| 0.4266 | 1300 | 0.0005 | - | |
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| 0.4431 | 1350 | 0.0004 | - | |
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| 0.4595 | 1400 | 0.0003 | - | |
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| 0.4759 | 1450 | 0.0003 | - | |
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| 0.4923 | 1500 | 0.0003 | - | |
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| 0.5087 | 1550 | 0.0003 | - | |
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| 0.5251 | 1600 | 0.0003 | - | |
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| 0.5415 | 1650 | 0.0003 | - | |
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| 0.5579 | 1700 | 0.0003 | - | |
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| 0.5743 | 1750 | 0.0003 | - | |
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| 0.5907 | 1800 | 0.0003 | - | |
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| 0.6072 | 1850 | 0.0002 | - | |
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| 0.6236 | 1900 | 0.0003 | - | |
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| 0.6400 | 1950 | 0.0002 | - | |
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| 0.6564 | 2000 | 0.0002 | - | |
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| 0.6728 | 2050 | 0.0002 | - | |
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| 0.6892 | 2100 | 0.0003 | - | |
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| 0.7056 | 2150 | 0.0002 | - | |
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| 0.7220 | 2200 | 0.0002 | - | |
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| 0.7384 | 2250 | 0.0002 | - | |
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| 0.7548 | 2300 | 0.0002 | - | |
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| 0.7713 | 2350 | 0.0002 | - | |
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| 0.7877 | 2400 | 0.0002 | - | |
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| 0.8041 | 2450 | 0.0002 | - | |
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| 0.8205 | 2500 | 0.0002 | - | |
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| 0.8369 | 2550 | 0.0002 | - | |
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| 0.8533 | 2600 | 0.0002 | - | |
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| 0.8697 | 2650 | 0.0002 | - | |
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| 0.8861 | 2700 | 0.0002 | - | |
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| 0.9025 | 2750 | 0.0002 | - | |
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| 0.9189 | 2800 | 0.0002 | - | |
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| 0.9353 | 2850 | 0.0002 | - | |
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| 0.9518 | 2900 | 0.0002 | - | |
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| 0.9682 | 2950 | 0.0002 | - | |
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| 0.9846 | 3000 | 0.0002 | - | |
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| **1.0** | **3047** | **-** | **0.0** | |
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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+cu121 |
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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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