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--- |
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base_model: BAAI/bge-large-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: I don't want to handle any filtering tasks. |
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- text: Show me all customers who have the last name 'Doe'. |
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- text: What tables are available for data analysis in starhub_data_asset? |
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- text: what do you think it is? |
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- text: Provide data_asset_001_pcc product category details. |
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inference: true |
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model-index: |
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- name: SetFit with BAAI/bge-large-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: 0.9818181818181818 |
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name: Accuracy |
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--- |
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# SetFit with BAAI/bge-large-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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>'Show me median Intangible Assets'</li><li>'Can I have sum Cost_Entertainment?'</li><li>'Get me min RevenueVariance_Actual_vs_Forecast.'</li></ul> | |
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| Lookup_1 | <ul><li>'Show me data_asset_kpi_cf details.'</li><li>'Retrieve data_asset_kpi_cf details.'</li><li>'Show M&A deal size by sector.'</li></ul> | |
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| Viewtables | <ul><li>'What tables are included in the starhub_data_asset database that are required for performing a basic data analysis?'</li><li>'What is the full list of tables available for use in queries within the starhub_data_asset database?'</li><li>'What are the table names within the starhub_data_asset database that enable data analysis of customer feedback?'</li></ul> | |
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| Tablejoin | <ul><li>'Is it possible to merge the Employees and Orders tables to see which employee handled each order?'</li><li>'Join data_asset_001_ta with data_asset_kpi_cf.'</li><li>'How can I connect the Customers and Orders tables to find customers who made purchases during a specific promotion?'</li></ul> | |
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| Lookup | <ul><li>'Filter by customers who have placed more than 3 orders and get me their email addresses.'</li><li>"Filter by customers in the city 'New York' and show me their phone numbers."</li><li>"Can you filter by employees who work in the 'Research' department?"</li></ul> | |
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| Generalreply | <ul><li>"Oh, I just stepped outside and it's actually quite lovely! The sun is shining and there's a light breeze. How about you?"</li><li>"One of my short-term goals is to learn a new skill, like coding or cooking. I also want to save up enough money for a weekend trip with friends. How about you, any short-term goals you're working towards?"</li><li>'Hey! My day is going pretty well, thanks for asking. How about yours?'</li></ul> | |
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| Rejection | <ul><li>'I have no interest in generating more data.'</li><li>"I don't want to engage in filtering operations."</li><li>"I'd rather not filter this dataset."</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** | 0.9818 | |
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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-large-en-v1.5-brahmaputra-iter-10-3rd") |
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# Run inference |
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preds = model("what do you think it is?") |
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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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### 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 | 1 | 8.7137 | 62 | |
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| Label | Training Sample Count | |
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|:-------------|:----------------------| |
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| Tablejoin | 128 | |
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| Rejection | 73 | |
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| Aggregation | 222 | |
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| Lookup | 55 | |
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| Generalreply | 75 | |
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| Viewtables | 76 | |
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| Lookup_1 | 157 | |
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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 | 0.2001 | - | |
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| 0.0022 | 50 | 0.1566 | - | |
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| 0.0045 | 100 | 0.0816 | - | |
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| 0.0067 | 150 | 0.0733 | - | |
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| 0.0089 | 200 | 0.0075 | - | |
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| 0.0112 | 250 | 0.0059 | - | |
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| 0.0134 | 300 | 0.0035 | - | |
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| 0.0156 | 350 | 0.0034 | - | |
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| 0.0179 | 400 | 0.0019 | - | |
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| 0.0201 | 450 | 0.0015 | - | |
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| 0.0223 | 500 | 0.0021 | - | |
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| 0.0246 | 550 | 0.003 | - | |
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| 0.0268 | 600 | 0.0021 | - | |
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| 0.0290 | 650 | 0.0011 | - | |
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| 0.0313 | 700 | 0.0015 | - | |
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| 0.0335 | 750 | 0.0011 | - | |
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| 0.0357 | 800 | 0.001 | - | |
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| 0.0380 | 850 | 0.001 | - | |
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| 0.0402 | 900 | 0.0012 | - | |
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| 0.0424 | 950 | 0.0012 | - | |
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| 0.0447 | 1000 | 0.0011 | - | |
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| 0.0469 | 1050 | 0.0008 | - | |
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| 0.0491 | 1100 | 0.0009 | - | |
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| 0.0514 | 1150 | 0.001 | - | |
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| 0.0536 | 1200 | 0.0008 | - | |
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| 0.0558 | 1250 | 0.0011 | - | |
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| 0.0581 | 1300 | 0.0009 | - | |
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| 0.0603 | 1350 | 0.001 | - | |
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| 0.0625 | 1400 | 0.0007 | - | |
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| 0.0647 | 1450 | 0.0008 | - | |
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| 0.0670 | 1500 | 0.0007 | - | |
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| 0.0692 | 1550 | 0.001 | - | |
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| 0.0714 | 1600 | 0.0007 | - | |
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| 0.0737 | 1650 | 0.0007 | - | |
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| 0.0759 | 1700 | 0.0006 | - | |
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| 0.0781 | 1750 | 0.0008 | - | |
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| 0.0804 | 1800 | 0.0006 | - | |
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| 0.0826 | 1850 | 0.0005 | - | |
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| 0.0848 | 1900 | 0.0006 | - | |
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| 0.0871 | 1950 | 0.0005 | - | |
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| 0.0893 | 2000 | 0.0007 | - | |
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| 0.0915 | 2050 | 0.0005 | - | |
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| 0.0938 | 2100 | 0.0006 | - | |
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| 0.0960 | 2150 | 0.0007 | - | |
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| 0.0982 | 2200 | 0.0005 | - | |
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| 0.1005 | 2250 | 0.0008 | - | |
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| 0.1027 | 2300 | 0.0005 | - | |
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| 0.1049 | 2350 | 0.0008 | - | |
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| 0.1072 | 2400 | 0.0007 | - | |
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| **0.1094** | **2450** | **0.0007** | **0.0094** | |
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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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