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--- |
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base_model: BAAI/bge-small-en-v1.5 |
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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: What’s the total number of orders placed by each customer? |
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- text: I like to read books and listen to music in my free time. How about you? |
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- text: Get company-wise intangible asset ratio. |
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- text: Show me data_asset_001_ta by product. |
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- text: Show me average asset value. |
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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: 0.9915254237288136 |
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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:** 7 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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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| Aggregation | <ul><li>'Please show med CostVariance_Actual_vs_Forecast.'</li><li>'Get me data_asset_001_kpm group by metrics.'</li><li>'Provide data_asset_kpi_cf group by quarter.'</li></ul> | |
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| Tablejoin | <ul><li>'Join data_asset_kpi_cf with data_asset_001_kpm tables.'</li><li>'Could you link the Products and Orders tables to track sales trends for different product categories?'</li><li>'Can I have a merge of income statement and key performance metrics tables?'</li></ul> | |
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| Lookup | <ul><li>"Filter by the 'Sales' department and show me the employees."</li><li>"Filter by the 'Toys' category and get me the product names."</li><li>'Can you get me the products with a price above 100?'</li></ul> | |
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| Rejection | <ul><li>"Let's avoid generating additional reports."</li><li>"I'd rather not filter this dataset."</li><li>"I'd prefer not to apply any filters."</li></ul> | |
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| Lookup_1 | <ul><li>'Show me key income statement metrics.'</li><li>'can I have kpm table'</li><li>'Retrieve data_asset_kpi_ma_product records.'</li></ul> | |
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| Generalreply | <ul><li>"Hey! It's going pretty well, thanks for asking. How about yours?"</li><li>'Not much, just taking it one day at a time. How about you?'</li><li>"'What is your favorite quote?'"</li></ul> | |
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| Viewtables | <ul><li>'What are the table names that relate to customer service in the starhub_data_asset database?'</li><li>'What tables are available in the starhub_data_asset database that can be joined to track user behavior?'</li><li>'What are the tables that are available for analysis in the starhub_data_asset database?'</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** | 0.9915 | |
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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("nazhan/bge-small-en-v1.5-brahmaputra-iter-10-3rd") |
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# Run inference |
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preds = model("Show me average asset value.") |
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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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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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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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*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 | 1 | 8.7839 | 62 | |
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| Label | Training Sample Count | |
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|:-------------|:----------------------| |
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| Tablejoin | 127 | |
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| Rejection | 76 | |
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| Aggregation | 281 | |
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| Lookup | 59 | |
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| Generalreply | 71 | |
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| Viewtables | 75 | |
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| Lookup_1 | 158 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (1, 1) |
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- max_steps: 2450 |
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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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| 0.0000 | 1 | 0.2317 | - | |
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| 0.0025 | 50 | 0.2478 | - | |
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| 0.0050 | 100 | 0.2213 | - | |
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| 0.0075 | 150 | 0.0779 | - | |
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| 0.0100 | 200 | 0.1089 | - | |
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| 0.0125 | 250 | 0.0372 | - | |
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| 0.0149 | 300 | 0.0219 | - | |
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| 0.0174 | 350 | 0.0344 | - | |
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| 0.0199 | 400 | 0.012 | - | |
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| 0.0224 | 450 | 0.0049 | - | |
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| 0.0249 | 500 | 0.0041 | - | |
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| 0.0274 | 550 | 0.0083 | - | |
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| 0.0299 | 600 | 0.0057 | - | |
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| 0.0324 | 650 | 0.0047 | - | |
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| 0.0349 | 700 | 0.0022 | - | |
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| 0.0374 | 750 | 0.0015 | - | |
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| 0.0399 | 800 | 0.0032 | - | |
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| 0.0423 | 850 | 0.002 | - | |
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| 0.0448 | 900 | 0.0028 | - | |
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| 0.0473 | 950 | 0.0017 | - | |
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| 0.0498 | 1000 | 0.0017 | - | |
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| 0.0523 | 1050 | 0.0027 | - | |
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| 0.0548 | 1100 | 0.0022 | - | |
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| 0.0573 | 1150 | 0.0018 | - | |
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| 0.0598 | 1200 | 0.001 | - | |
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| 0.0623 | 1250 | 0.002 | - | |
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| 0.0648 | 1300 | 0.001 | - | |
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| 0.0673 | 1350 | 0.0013 | - | |
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| 0.0697 | 1400 | 0.0012 | - | |
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| 0.0722 | 1450 | 0.0018 | - | |
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| 0.0747 | 1500 | 0.0012 | - | |
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| 0.0772 | 1550 | 0.0016 | - | |
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| 0.0797 | 1600 | 0.0012 | - | |
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| 0.0822 | 1650 | 0.0016 | - | |
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| 0.0847 | 1700 | 0.0027 | - | |
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| 0.0872 | 1750 | 0.0014 | - | |
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| 0.0897 | 1800 | 0.0011 | - | |
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| 0.0922 | 1850 | 0.0011 | - | |
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| 0.0947 | 1900 | 0.0012 | - | |
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| 0.0971 | 1950 | 0.0014 | - | |
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| 0.0996 | 2000 | 0.0014 | - | |
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| 0.1021 | 2050 | 0.0015 | - | |
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| 0.1046 | 2100 | 0.0009 | - | |
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| 0.1071 | 2150 | 0.0015 | - | |
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| 0.1096 | 2200 | 0.0013 | - | |
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| 0.1121 | 2250 | 0.0013 | - | |
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| 0.1146 | 2300 | 0.001 | - | |
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| 0.1171 | 2350 | 0.0017 | - | |
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| 0.1196 | 2400 | 0.0013 | - | |
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| **0.1221** | **2450** | **0.0008** | **0.0323** | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.11.9 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.7.0 |
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- Transformers: 4.42.4 |
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- PyTorch: 2.4.0+cu121 |
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- Datasets: 2.21.0 |
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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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