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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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base_model: BAAI/bge-small-en-v1.5 |
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metrics: |
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- accuracy |
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widget: |
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- text: sales affects ceo pay |
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- text: time affects entrepreneurship intention |
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- text: operations planning affects entrepreneurship intention |
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- text: entrepreneurial self-efficacy affects entrepreneurship intention |
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- text: empirical training affects entrepreneurship intention |
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pipeline_tag: text-classification |
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inference: true |
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model-index: |
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- name: SetFit with BAAI/bge-small-en-v1.5 |
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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: 0.9058823529411765 |
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name: Accuracy |
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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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| 1 | <ul><li>'board diversity affects ceo pay'</li><li>'perceptions of formal learning affects entrepreneurship intention'</li><li>'proactiveness affects entrepreneurship intention'</li></ul> | |
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| 0 | <ul><li>'sales and takeovers affects entrepreneurship intention'</li><li>'uk affects entrepreneurship intention'</li><li>'economics affects entrepreneurship intention'</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** | 0.9059 | |
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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("abehandlerorg/setfit") |
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# Run inference |
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preds = model("sales affects ceo pay") |
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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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### 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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### Recommendations |
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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 | 4 | 5.4307 | 12 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 168 | |
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| 1 | 171 | |
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### Training Hyperparameters |
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- batch_size: (32, 32) |
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- num_epochs: (4, 4) |
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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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| 0.0006 | 1 | 0.3133 | - | |
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| 0.0277 | 50 | 0.289 | - | |
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| 0.0553 | 100 | 0.2506 | - | |
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| 0.0830 | 150 | 0.2243 | - | |
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| 0.1107 | 200 | 0.2388 | - | |
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| 0.1384 | 250 | 0.2084 | - | |
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| 0.1660 | 300 | 0.1316 | - | |
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| 0.1937 | 350 | 0.0142 | - | |
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| 0.2214 | 400 | 0.0065 | - | |
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| 0.2490 | 450 | 0.0037 | - | |
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| 0.2767 | 500 | 0.003 | - | |
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| 0.3044 | 550 | 0.002 | - | |
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| 0.3320 | 600 | 0.0018 | - | |
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| 0.3597 | 650 | 0.0026 | - | |
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| 0.3874 | 700 | 0.0013 | - | |
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| 0.4151 | 750 | 0.0012 | - | |
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| 0.4427 | 800 | 0.0284 | - | |
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| 0.4704 | 850 | 0.0145 | - | |
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| 0.4981 | 900 | 0.0053 | - | |
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| 0.5257 | 950 | 0.0075 | - | |
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| 0.5534 | 1000 | 0.005 | - | |
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| 0.5811 | 1050 | 0.0008 | - | |
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| 0.6087 | 1100 | 0.0008 | - | |
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| 0.6364 | 1150 | 0.0008 | - | |
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| 0.6641 | 1200 | 0.0007 | - | |
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| 0.6918 | 1250 | 0.0008 | - | |
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| 0.7194 | 1300 | 0.0009 | - | |
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| 0.7471 | 1350 | 0.0007 | - | |
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| 0.7748 | 1400 | 0.0008 | - | |
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| 0.8024 | 1450 | 0.0006 | - | |
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| 0.8301 | 1500 | 0.0006 | - | |
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| 0.8578 | 1550 | 0.0192 | - | |
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| 0.8854 | 1600 | 0.0005 | - | |
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| 0.9131 | 1650 | 0.002 | - | |
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| 0.9408 | 1700 | 0.0204 | - | |
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| 0.9685 | 1750 | 0.0039 | - | |
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| 0.9961 | 1800 | 0.0007 | - | |
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| 1.0238 | 1850 | 0.0005 | - | |
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| 1.0515 | 1900 | 0.0004 | - | |
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| 1.0791 | 1950 | 0.0005 | - | |
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| 1.1068 | 2000 | 0.0006 | - | |
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| 1.1345 | 2050 | 0.0004 | - | |
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| 1.1621 | 2100 | 0.0006 | - | |
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| 1.1898 | 2150 | 0.0004 | - | |
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| 1.2175 | 2200 | 0.0004 | - | |
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| 1.2452 | 2250 | 0.0018 | - | |
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| 1.2728 | 2300 | 0.0041 | - | |
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| 1.3005 | 2350 | 0.0004 | - | |
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| 1.3282 | 2400 | 0.0107 | - | |
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| 1.3558 | 2450 | 0.0005 | - | |
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| 1.3835 | 2500 | 0.0004 | - | |
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| 1.4112 | 2550 | 0.0004 | - | |
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| 1.4388 | 2600 | 0.0167 | - | |
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| 1.4665 | 2650 | 0.0068 | - | |
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| 1.4942 | 2700 | 0.0004 | - | |
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| 1.5219 | 2750 | 0.0064 | - | |
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| 1.5495 | 2800 | 0.0041 | - | |
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| 1.5772 | 2850 | 0.0004 | - | |
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| 1.6049 | 2900 | 0.0003 | - | |
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| 1.6325 | 2950 | 0.0004 | - | |
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| 1.6602 | 3000 | 0.0004 | - | |
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| 1.6879 | 3050 | 0.0003 | - | |
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| 1.7156 | 3100 | 0.0057 | - | |
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| 1.7432 | 3150 | 0.0044 | - | |
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| 1.7709 | 3200 | 0.0004 | - | |
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| 1.7986 | 3250 | 0.0166 | - | |
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| 1.8262 | 3300 | 0.0004 | - | |
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| 1.8539 | 3350 | 0.0032 | - | |
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| 1.8816 | 3400 | 0.0133 | - | |
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| 1.9092 | 3450 | 0.0003 | - | |
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| 1.9369 | 3500 | 0.0003 | - | |
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| 1.9646 | 3550 | 0.0052 | - | |
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| 1.9923 | 3600 | 0.0004 | - | |
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| 2.0199 | 3650 | 0.004 | - | |
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| 2.0476 | 3700 | 0.0003 | - | |
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| 2.0753 | 3750 | 0.0054 | - | |
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| 2.1029 | 3800 | 0.0057 | - | |
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| 2.1306 | 3850 | 0.0004 | - | |
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| 2.1583 | 3900 | 0.0272 | - | |
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| 2.1859 | 3950 | 0.0003 | - | |
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| 2.2136 | 4000 | 0.006 | - | |
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| 2.2413 | 4050 | 0.0044 | - | |
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| 2.2690 | 4100 | 0.0003 | - | |
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| 2.2966 | 4150 | 0.0167 | - | |
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| 2.3243 | 4200 | 0.0048 | - | |
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| 2.3520 | 4250 | 0.0086 | - | |
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| 2.3796 | 4300 | 0.0051 | - | |
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| 2.4073 | 4350 | 0.0003 | - | |
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| 2.4350 | 4400 | 0.0037 | - | |
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| 2.4626 | 4450 | 0.0003 | - | |
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| 2.4903 | 4500 | 0.0021 | - | |
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| 2.5180 | 4550 | 0.0003 | - | |
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| 2.5457 | 4600 | 0.004 | - | |
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| 2.5733 | 4650 | 0.0025 | - | |
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| 2.6010 | 4700 | 0.0003 | - | |
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| 2.6287 | 4750 | 0.0003 | - | |
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| 2.6563 | 4800 | 0.0003 | - | |
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| 2.6840 | 4850 | 0.0031 | - | |
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| 2.7117 | 4900 | 0.0168 | - | |
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| 2.7393 | 4950 | 0.0019 | - | |
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| 2.7670 | 5000 | 0.004 | - | |
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| 2.7947 | 5050 | 0.0003 | - | |
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| 2.8224 | 5100 | 0.0003 | - | |
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| 2.8500 | 5150 | 0.003 | - | |
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| 2.8777 | 5200 | 0.0003 | - | |
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| 2.9054 | 5250 | 0.0003 | - | |
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| 2.9330 | 5300 | 0.0171 | - | |
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| 2.9607 | 5350 | 0.0003 | - | |
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| 2.9884 | 5400 | 0.0162 | - | |
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| 3.0160 | 5450 | 0.0143 | - | |
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| 3.0437 | 5500 | 0.0134 | - | |
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| 3.0714 | 5550 | 0.0133 | - | |
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| 3.0991 | 5600 | 0.0003 | - | |
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| 3.1267 | 5650 | 0.0003 | - | |
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| 3.1544 | 5700 | 0.0093 | - | |
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| 3.1821 | 5750 | 0.0003 | - | |
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| 3.2097 | 5800 | 0.0003 | - | |
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| 3.2374 | 5850 | 0.0003 | - | |
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| 3.2651 | 5900 | 0.0003 | - | |
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| 3.2928 | 5950 | 0.0003 | - | |
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| 3.3204 | 6000 | 0.0029 | - | |
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| 3.3481 | 6050 | 0.0126 | - | |
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| 3.3758 | 6100 | 0.0003 | - | |
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| 3.4034 | 6150 | 0.0002 | - | |
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| 3.4311 | 6200 | 0.0003 | - | |
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| 3.4588 | 6250 | 0.0062 | - | |
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| 3.4864 | 6300 | 0.0002 | - | |
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| 3.5141 | 6350 | 0.0002 | - | |
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| 3.5418 | 6400 | 0.0003 | - | |
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| 3.5695 | 6450 | 0.0002 | - | |
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| 3.5971 | 6500 | 0.0041 | - | |
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| 3.6248 | 6550 | 0.0465 | - | |
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| 3.6525 | 6600 | 0.0148 | - | |
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| 3.6801 | 6650 | 0.0181 | - | |
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| 3.7078 | 6700 | 0.0037 | - | |
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| 3.7355 | 6750 | 0.0002 | - | |
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| 3.7631 | 6800 | 0.0003 | - | |
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| 3.7908 | 6850 | 0.0003 | - | |
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| 3.8185 | 6900 | 0.0034 | - | |
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| 3.8462 | 6950 | 0.0002 | - | |
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| 3.8738 | 7000 | 0.0148 | - | |
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| 3.9015 | 7050 | 0.0002 | - | |
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| 3.9292 | 7100 | 0.0003 | - | |
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| 3.9568 | 7150 | 0.0002 | - | |
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| 3.9845 | 7200 | 0.0003 | - | |
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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: 2.7.0 |
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- Transformers: 4.40.1 |
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- PyTorch: 2.2.1+cu121 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.19.1 |
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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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