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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: you're very lucky. |
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- text: Show me operating cash flow trends. |
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- text: Join data_asset_kpi_is and data_asset_kpi_cf tables. |
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- text: Can I have max EBIT_Margin? |
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- text: I'm not inclined to generate further data sets. |
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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: 0.9829059829059829 |
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name: Accuracy |
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--- |
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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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<!-- - **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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| Lookup_1 | <ul><li>'Analyze product category revenue impact.'</li><li>'Analyze Product-wise Financial Performance Metrics.'</li><li>'Get M&A deal size by company.'</li></ul> | |
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| Aggregation | <ul><li>'Group the products by color and find the average price for each color.'</li><li>'Get me count Product.'</li><li>'Show me forecast accuracy and group by version.'</li></ul> | |
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| Lookup | <ul><li>'What are the products with a price below 20?'</li><li>'Can you get me the products that are out of stock?'</li><li>'Get me the list of employees who joined the company after January 2023.'</li></ul> | |
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| Viewtables | <ul><li>'What are the different types of tables that can be found within the starhub_data_asset database?'</li><li>'What is the complete list of tables in the starhub_data_asset database that can be accessed without needing to perform any table joining operations?'</li><li>'What is the list of tables that a new user should familiarize themselves with when accessing the starhub_data_asset database?'</li></ul> | |
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| Tablejoin | <ul><li>'Can you join the Products and Orders tables to track revenue by product category?'</li><li>'Could you combine table data from Orders and Products to identify which products were ordered most frequently?'</li><li>'Show me a join of key performance metrics and cash flow tables.'</li></ul> | |
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| Generalreply | <ul><li>"Oh, I'm a big fan of indie rock. What about you? What's your favorite type of music?"</li><li>'It was pretty good! How about yours?'</li><li>"Oh, that's a tough question! I have a few favorites, but if I had to pick just one, it would be The Shawshank Redemption. What about you, what's your favorite movie?"</li></ul> | |
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| Rejection | <ul><li>"I don't need to filter this data set."</li><li>"Let's not generate more data entries."</li><li>"Please don't filter the list."</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** | 0.9829 | |
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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-9-2nd-1-epoch") |
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# Run inference |
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preds = model("you're very lucky.") |
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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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--> |
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<!-- |
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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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--> |
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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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--> |
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<!-- |
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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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--> |
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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 | 8.8397 | 53 | |
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| Label | Training Sample Count | |
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|:-------------|:----------------------| |
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| Tablejoin | 129 | |
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| Rejection | 69 | |
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| Aggregation | 282 | |
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| Lookup | 64 | |
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| Generalreply | 69 | |
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| Viewtables | 76 | |
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| Lookup_1 | 147 | |
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|
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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: -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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| 0.0000 | 1 | 0.23 | - | |
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| 0.0014 | 50 | 0.196 | - | |
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| 0.0028 | 100 | 0.1679 | - | |
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| 0.0043 | 150 | 0.156 | - | |
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| 0.0057 | 200 | 0.2 | - | |
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| 0.0071 | 250 | 0.0765 | - | |
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| 0.0085 | 300 | 0.167 | - | |
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| 0.0100 | 350 | 0.1154 | - | |
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| 0.0114 | 400 | 0.0625 | - | |
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| 0.0128 | 450 | 0.0666 | - | |
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| 0.0142 | 500 | 0.0515 | - | |
|
| 0.0157 | 550 | 0.0178 | - | |
|
| 0.0171 | 600 | 0.0068 | - | |
|
| 0.0185 | 650 | 0.0174 | - | |
|
| 0.0199 | 700 | 0.0136 | - | |
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| 0.0214 | 750 | 0.0066 | - | |
|
| 0.0228 | 800 | 0.0052 | - | |
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| 0.0242 | 850 | 0.0045 | - | |
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| 0.0256 | 900 | 0.003 | - | |
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| 0.0271 | 950 | 0.0031 | - | |
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| 0.0285 | 1000 | 0.0035 | - | |
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| 0.0299 | 1050 | 0.0032 | - | |
|
| 0.0313 | 1100 | 0.0031 | - | |
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| 0.0328 | 1150 | 0.0029 | - | |
|
| 0.0342 | 1200 | 0.0023 | - | |
|
| 0.0356 | 1250 | 0.0012 | - | |
|
| 0.0370 | 1300 | 0.0025 | - | |
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| 0.0385 | 1350 | 0.0019 | - | |
|
| 0.0399 | 1400 | 0.0023 | - | |
|
| 0.0413 | 1450 | 0.0016 | - | |
|
| 0.0427 | 1500 | 0.0018 | - | |
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| 0.0441 | 1550 | 0.0019 | - | |
|
| 0.0456 | 1600 | 0.0012 | - | |
|
| 0.0470 | 1650 | 0.0012 | - | |
|
| 0.0484 | 1700 | 0.0013 | - | |
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| 0.0498 | 1750 | 0.0011 | - | |
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| 0.0513 | 1800 | 0.001 | - | |
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| 0.0527 | 1850 | 0.0013 | - | |
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| 0.0541 | 1900 | 0.0014 | - | |
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| 0.0555 | 1950 | 0.0008 | - | |
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| 0.0570 | 2000 | 0.0009 | - | |
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| 0.0584 | 2050 | 0.0009 | - | |
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| 0.0598 | 2100 | 0.0009 | - | |
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| 0.0612 | 2150 | 0.0012 | - | |
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| 0.0627 | 2200 | 0.0008 | - | |
|
| 0.0641 | 2250 | 0.0011 | - | |
|
| 0.0655 | 2300 | 0.0006 | - | |
|
| 0.0669 | 2350 | 0.0011 | - | |
|
| 0.0684 | 2400 | 0.0007 | - | |
|
| 0.0698 | 2450 | 0.0009 | - | |
|
| 0.0712 | 2500 | 0.0007 | - | |
|
| 0.0726 | 2550 | 0.0005 | - | |
|
| 0.0741 | 2600 | 0.0006 | - | |
|
| 0.0755 | 2650 | 0.0007 | - | |
|
| 0.0769 | 2700 | 0.0008 | - | |
|
| 0.0783 | 2750 | 0.0007 | - | |
|
| 0.0798 | 2800 | 0.0007 | - | |
|
| 0.0812 | 2850 | 0.0007 | - | |
|
| 0.0826 | 2900 | 0.0008 | - | |
|
| 0.0840 | 2950 | 0.0006 | - | |
|
| 0.0855 | 3000 | 0.0006 | - | |
|
| 0.0869 | 3050 | 0.0006 | - | |
|
| 0.0883 | 3100 | 0.0005 | - | |
|
| 0.0897 | 3150 | 0.0007 | - | |
|
| 0.0911 | 3200 | 0.0005 | - | |
|
| 0.0926 | 3250 | 0.0007 | - | |
|
| 0.0940 | 3300 | 0.0007 | - | |
|
| 0.0954 | 3350 | 0.0006 | - | |
|
| 0.0968 | 3400 | 0.0007 | - | |
|
| 0.0983 | 3450 | 0.0005 | - | |
|
| 0.0997 | 3500 | 0.0005 | - | |
|
| 0.1011 | 3550 | 0.0005 | - | |
|
| 0.1025 | 3600 | 0.0004 | - | |
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| 0.1040 | 3650 | 0.0003 | - | |
|
| 0.1054 | 3700 | 0.0005 | - | |
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| 0.1068 | 3750 | 0.0004 | - | |
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| 0.1082 | 3800 | 0.0005 | - | |
|
| 0.1097 | 3850 | 0.0004 | - | |
|
| 0.1111 | 3900 | 0.0004 | - | |
|
| 0.1125 | 3950 | 0.0003 | - | |
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| 0.1139 | 4000 | 0.0004 | - | |
|
| 0.1154 | 4050 | 0.0003 | - | |
|
| 0.1168 | 4100 | 0.1163 | - | |
|
| 0.1182 | 4150 | 0.0054 | - | |
|
| 0.1196 | 4200 | 0.0317 | - | |
|
| 0.1211 | 4250 | 0.0009 | - | |
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| 0.1225 | 4300 | 0.0005 | - | |
|
| 0.1239 | 4350 | 0.0008 | - | |
|
| 0.1253 | 4400 | 0.0007 | - | |
|
| 0.1268 | 4450 | 0.0004 | - | |
|
| 0.1282 | 4500 | 0.0006 | - | |
|
| 0.1296 | 4550 | 0.0004 | - | |
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| 0.1310 | 4600 | 0.0003 | - | |
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| 0.1324 | 4650 | 0.0004 | - | |
|
| 0.1339 | 4700 | 0.0005 | - | |
|
| 0.1353 | 4750 | 0.0003 | - | |
|
| 0.1367 | 4800 | 0.0004 | - | |
|
| 0.1381 | 4850 | 0.0004 | - | |
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| 0.1396 | 4900 | 0.0002 | - | |
|
| 0.1410 | 4950 | 0.0005 | - | |
|
| 0.1424 | 5000 | 0.0003 | - | |
|
| 0.1438 | 5050 | 0.0004 | - | |
|
| 0.1453 | 5100 | 0.0004 | - | |
|
| 0.1467 | 5150 | 0.0003 | - | |
|
| 0.1481 | 5200 | 0.0003 | - | |
|
| 0.1495 | 5250 | 0.0003 | - | |
|
| 0.1510 | 5300 | 0.0005 | - | |
|
| 0.1524 | 5350 | 0.0004 | - | |
|
| 0.1538 | 5400 | 0.0002 | - | |
|
| 0.1552 | 5450 | 0.0003 | - | |
|
| 0.1567 | 5500 | 0.0003 | - | |
|
| 0.1581 | 5550 | 0.0002 | - | |
|
| 0.1595 | 5600 | 0.0002 | - | |
|
| 0.1609 | 5650 | 0.0003 | - | |
|
| 0.1624 | 5700 | 0.0003 | - | |
|
| 0.1638 | 5750 | 0.0003 | - | |
|
| 0.1652 | 5800 | 0.0002 | - | |
|
| 0.1666 | 5850 | 0.0003 | - | |
|
| 0.1681 | 5900 | 0.0003 | - | |
|
| 0.1695 | 5950 | 0.0003 | - | |
|
| 0.1709 | 6000 | 0.0002 | - | |
|
| 0.1723 | 6050 | 0.0002 | - | |
|
| 0.1737 | 6100 | 0.0002 | - | |
|
| 0.1752 | 6150 | 0.0002 | - | |
|
| 0.1766 | 6200 | 0.0003 | - | |
|
| 0.1780 | 6250 | 0.0002 | - | |
|
| 0.1794 | 6300 | 0.0003 | - | |
|
| 0.1809 | 6350 | 0.0002 | - | |
|
| 0.1823 | 6400 | 0.0003 | - | |
|
| 0.1837 | 6450 | 0.0003 | - | |
|
| 0.1851 | 6500 | 0.0002 | - | |
|
| 0.1866 | 6550 | 0.0002 | - | |
|
| 0.1880 | 6600 | 0.0004 | - | |
|
| 0.1894 | 6650 | 0.0002 | - | |
|
| 0.1908 | 6700 | 0.0002 | - | |
|
| 0.1923 | 6750 | 0.0002 | - | |
|
| 0.1937 | 6800 | 0.0002 | - | |
|
| 0.1951 | 6850 | 0.0002 | - | |
|
| 0.1965 | 6900 | 0.0002 | - | |
|
| 0.1980 | 6950 | 0.0002 | - | |
|
| 0.1994 | 7000 | 0.0002 | - | |
|
| 0.2008 | 7050 | 0.0002 | - | |
|
| 0.2022 | 7100 | 0.0002 | - | |
|
| 0.2037 | 7150 | 0.0003 | - | |
|
| 0.2051 | 7200 | 0.0002 | - | |
|
| 0.2065 | 7250 | 0.0002 | - | |
|
| 0.2079 | 7300 | 0.0002 | - | |
|
| 0.2094 | 7350 | 0.0002 | - | |
|
| 0.2108 | 7400 | 0.0002 | - | |
|
| 0.2122 | 7450 | 0.0002 | - | |
|
| 0.2136 | 7500 | 0.0002 | - | |
|
| 0.2151 | 7550 | 0.0002 | - | |
|
| 0.2165 | 7600 | 0.0002 | - | |
|
| 0.2179 | 7650 | 0.0002 | - | |
|
| 0.2193 | 7700 | 0.0002 | - | |
|
| 0.2207 | 7750 | 0.0002 | - | |
|
| 0.2222 | 7800 | 0.0001 | - | |
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| 0.2236 | 7850 | 0.0002 | - | |
|
| 0.2250 | 7900 | 0.0002 | - | |
|
| 0.2264 | 7950 | 0.0002 | - | |
|
| 0.2279 | 8000 | 0.0002 | - | |
|
| 0.2293 | 8050 | 0.0002 | - | |
|
| 0.2307 | 8100 | 0.0002 | - | |
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| 0.2321 | 8150 | 0.0002 | - | |
|
| 0.2336 | 8200 | 0.0002 | - | |
|
| 0.2350 | 8250 | 0.0004 | - | |
|
| 0.2364 | 8300 | 0.0001 | - | |
|
| 0.2378 | 8350 | 0.0002 | - | |
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| 0.2393 | 8400 | 0.0001 | - | |
|
| 0.2407 | 8450 | 0.0002 | - | |
|
| 0.2421 | 8500 | 0.0001 | - | |
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| 0.2435 | 8550 | 0.0002 | - | |
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| 0.2450 | 8600 | 0.0002 | - | |
|
| 0.2464 | 8650 | 0.0002 | - | |
|
| 0.2478 | 8700 | 0.0001 | - | |
|
| 0.2492 | 8750 | 0.0001 | - | |
|
| 0.2507 | 8800 | 0.0001 | - | |
|
| 0.2521 | 8850 | 0.0002 | - | |
|
| 0.2535 | 8900 | 0.0002 | - | |
|
| 0.2549 | 8950 | 0.0002 | - | |
|
| 0.2564 | 9000 | 0.0002 | - | |
|
| 0.2578 | 9050 | 0.0001 | - | |
|
| 0.2592 | 9100 | 0.0001 | - | |
|
| 0.2606 | 9150 | 0.0003 | - | |
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| 0.2620 | 9200 | 0.0001 | - | |
|
| 0.2635 | 9250 | 0.0001 | - | |
|
| 0.2649 | 9300 | 0.0002 | - | |
|
| 0.2663 | 9350 | 0.0001 | - | |
|
| 0.2677 | 9400 | 0.0001 | - | |
|
| 0.2692 | 9450 | 0.0001 | - | |
|
| 0.2706 | 9500 | 0.0002 | - | |
|
| 0.2720 | 9550 | 0.0002 | - | |
|
| 0.2734 | 9600 | 0.0002 | - | |
|
| 0.2749 | 9650 | 0.0001 | - | |
|
| 0.2763 | 9700 | 0.0002 | - | |
|
| 0.2777 | 9750 | 0.0001 | - | |
|
| 0.2791 | 9800 | 0.0001 | - | |
|
| 0.2806 | 9850 | 0.0001 | - | |
|
| 0.2820 | 9900 | 0.0002 | - | |
|
| 0.2834 | 9950 | 0.0002 | - | |
|
| 0.2848 | 10000 | 0.0001 | - | |
|
| 0.2863 | 10050 | 0.0001 | - | |
|
| 0.2877 | 10100 | 0.0001 | - | |
|
| 0.2891 | 10150 | 0.0002 | - | |
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| 0.2905 | 10200 | 0.0001 | - | |
|
| 0.2920 | 10250 | 0.0002 | - | |
|
| 0.2934 | 10300 | 0.0001 | - | |
|
| 0.2948 | 10350 | 0.0002 | - | |
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| 0.2962 | 10400 | 0.0001 | - | |
|
| 0.2977 | 10450 | 0.0001 | - | |
|
| 0.2991 | 10500 | 0.0001 | - | |
|
| 0.3005 | 10550 | 0.0001 | - | |
|
| 0.3019 | 10600 | 0.0001 | - | |
|
| 0.3033 | 10650 | 0.0001 | - | |
|
| 0.3048 | 10700 | 0.0001 | - | |
|
| 0.3062 | 10750 | 0.0001 | - | |
|
| 0.3076 | 10800 | 0.0001 | - | |
|
| 0.3090 | 10850 | 0.0001 | - | |
|
| 0.3105 | 10900 | 0.0001 | - | |
|
| 0.3119 | 10950 | 0.0001 | - | |
|
| 0.3133 | 11000 | 0.0001 | - | |
|
| 0.3147 | 11050 | 0.0001 | - | |
|
| 0.3162 | 11100 | 0.0001 | - | |
|
| 0.3176 | 11150 | 0.0001 | - | |
|
| 0.3190 | 11200 | 0.0001 | - | |
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| 0.3204 | 11250 | 0.0001 | - | |
|
| 0.3219 | 11300 | 0.0001 | - | |
|
| 0.3233 | 11350 | 0.0001 | - | |
|
| 0.3247 | 11400 | 0.0002 | - | |
|
| 0.3261 | 11450 | 0.0001 | - | |
|
| 0.3276 | 11500 | 0.0001 | - | |
|
| 0.3290 | 11550 | 0.0001 | - | |
|
| 0.3304 | 11600 | 0.0001 | - | |
|
| 0.3318 | 11650 | 0.0001 | - | |
|
| 0.3333 | 11700 | 0.0002 | - | |
|
| 0.3347 | 11750 | 0.0001 | - | |
|
| 0.3361 | 11800 | 0.0001 | - | |
|
| 0.3375 | 11850 | 0.0001 | - | |
|
| 0.3390 | 11900 | 0.0002 | - | |
|
| 0.3404 | 11950 | 0.0001 | - | |
|
| 0.3418 | 12000 | 0.0001 | - | |
|
| 0.3432 | 12050 | 0.0002 | - | |
|
| 0.3447 | 12100 | 0.0001 | - | |
|
| 0.3461 | 12150 | 0.0001 | - | |
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| 0.3475 | 12200 | 0.0001 | - | |
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| 0.3489 | 12250 | 0.0003 | - | |
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| 0.3503 | 12300 | 0.0003 | - | |
|
| 0.3518 | 12350 | 0.0003 | - | |
|
| 0.3532 | 12400 | 0.0269 | - | |
|
| 0.3546 | 12450 | 0.0475 | - | |
|
| 0.3560 | 12500 | 0.0004 | - | |
|
| 0.3575 | 12550 | 0.0003 | - | |
|
| 0.3589 | 12600 | 0.0005 | - | |
|
| 0.3603 | 12650 | 0.0003 | - | |
|
| 0.3617 | 12700 | 0.0001 | - | |
|
| 0.3632 | 12750 | 0.0002 | - | |
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| 0.3646 | 12800 | 0.0003 | - | |
|
| 0.3660 | 12850 | 0.0002 | - | |
|
| 0.3674 | 12900 | 0.0001 | - | |
|
| 0.3689 | 12950 | 0.0004 | - | |
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| 0.3703 | 13000 | 0.0002 | - | |
|
| 0.3717 | 13050 | 0.0002 | - | |
|
| 0.3731 | 13100 | 0.0003 | - | |
|
| 0.3746 | 13150 | 0.0002 | - | |
|
| 0.3760 | 13200 | 0.0003 | - | |
|
| 0.3774 | 13250 | 0.0003 | - | |
|
| 0.3788 | 13300 | 0.0001 | - | |
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| 0.3803 | 13350 | 0.0002 | - | |
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| 0.3817 | 13400 | 0.0002 | - | |
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| 0.3831 | 13450 | 0.0002 | - | |
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| 0.3845 | 13500 | 0.0002 | - | |
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| 0.3860 | 13550 | 0.0002 | - | |
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| 0.3874 | 13600 | 0.0002 | - | |
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| 0.3888 | 13650 | 0.0001 | - | |
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| 0.3902 | 13700 | 0.0001 | - | |
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| 0.3916 | 13750 | 0.0002 | - | |
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| 0.3931 | 13800 | 0.0003 | - | |
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| 0.3945 | 13850 | 0.0002 | - | |
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| 0.3959 | 13900 | 0.0002 | - | |
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| 0.3973 | 13950 | 0.0001 | - | |
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| 0.3988 | 14000 | 0.0001 | - | |
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| 0.4002 | 14050 | 0.0001 | - | |
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| 0.4016 | 14100 | 0.0002 | - | |
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| 0.4030 | 14150 | 0.0002 | - | |
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| 0.4045 | 14200 | 0.0001 | - | |
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| 0.4059 | 14250 | 0.0001 | - | |
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| 0.4073 | 14300 | 0.0001 | - | |
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| 0.4087 | 14350 | 0.0001 | - | |
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| 0.4102 | 14400 | 0.0003 | - | |
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| 0.4116 | 14450 | 0.0002 | - | |
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| 0.4130 | 14500 | 0.0001 | - | |
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| 0.4144 | 14550 | 0.0002 | - | |
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| 0.4159 | 14600 | 0.0002 | - | |
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| 0.4173 | 14650 | 0.0001 | - | |
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| 0.4187 | 14700 | 0.0001 | - | |
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| 0.4201 | 14750 | 0.0001 | - | |
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| 0.4216 | 14800 | 0.0001 | - | |
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| 0.4230 | 14850 | 0.0001 | - | |
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| 0.4244 | 14900 | 0.0001 | - | |
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| 0.4258 | 14950 | 0.0002 | - | |
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| 0.4273 | 15000 | 0.0001 | - | |
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| 0.4287 | 15050 | 0.0001 | - | |
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| 0.4301 | 15100 | 0.0001 | - | |
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| 0.4315 | 15150 | 0.0001 | - | |
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| 0.4329 | 15200 | 0.0001 | - | |
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| 0.4344 | 15250 | 0.0001 | - | |
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| 0.4358 | 15300 | 0.0001 | - | |
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| 0.4372 | 15350 | 0.0001 | - | |
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| 0.4386 | 15400 | 0.0001 | - | |
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| 0.4401 | 15450 | 0.0001 | - | |
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| 0.4429 | 15550 | 0.0001 | - | |
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| 0.4443 | 15600 | 0.0001 | - | |
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| 0.4458 | 15650 | 0.0001 | - | |
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| 0.4472 | 15700 | 0.0001 | - | |
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| 0.4486 | 15750 | 0.0001 | - | |
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| 0.4500 | 15800 | 0.0001 | - | |
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| 0.4515 | 15850 | 0.0017 | - | |
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| 0.4529 | 15900 | 0.0007 | - | |
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| 0.4543 | 15950 | 0.0009 | - | |
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| 0.4557 | 16000 | 0.0004 | - | |
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| 0.4572 | 16050 | 0.0006 | - | |
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| 0.4586 | 16100 | 0.0003 | - | |
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| 0.4600 | 16150 | 0.0003 | - | |
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| 0.4614 | 16200 | 0.0003 | - | |
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| 0.4629 | 16250 | 0.0003 | - | |
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| 0.4643 | 16300 | 0.0002 | - | |
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| 0.4657 | 16350 | 0.0002 | - | |
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| 0.4671 | 16400 | 0.0002 | - | |
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| 0.4686 | 16450 | 0.0002 | - | |
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| 0.4700 | 16500 | 0.0001 | - | |
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| 0.4714 | 16550 | 0.0002 | - | |
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| 0.4728 | 16600 | 0.0002 | - | |
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| 0.4743 | 16650 | 0.0001 | - | |
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| 0.4757 | 16700 | 0.0002 | - | |
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| 0.4771 | 16750 | 0.0001 | - | |
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| 0.4785 | 16800 | 0.0001 | - | |
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| 0.4799 | 16850 | 0.0001 | - | |
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| 0.4814 | 16900 | 0.0004 | - | |
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| 0.4828 | 16950 | 0.0001 | - | |
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| 0.4842 | 17000 | 0.0002 | - | |
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| 0.4856 | 17050 | 0.0001 | - | |
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| 0.4871 | 17100 | 0.0001 | - | |
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| 0.4885 | 17150 | 0.0002 | - | |
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| 0.4899 | 17200 | 0.0001 | - | |
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| 0.4928 | 17300 | 0.0001 | - | |
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| 0.4942 | 17350 | 0.0001 | - | |
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| 0.4956 | 17400 | 0.0001 | - | |
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| 0.4970 | 17450 | 0.0001 | - | |
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| 0.4985 | 17500 | 0.0001 | - | |
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| 0.4999 | 17550 | 0.0001 | - | |
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| 0.5013 | 17600 | 0.0002 | - | |
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| 0.5027 | 17650 | 0.0001 | - | |
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| 0.5042 | 17700 | 0.0001 | - | |
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| 0.5056 | 17750 | 0.0001 | - | |
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| 0.5070 | 17800 | 0.0001 | - | |
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| 0.5084 | 17850 | 0.0001 | - | |
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| 0.5099 | 17900 | 0.0001 | - | |
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| 0.5113 | 17950 | 0.0001 | - | |
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| 0.5127 | 18000 | 0.0001 | - | |
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| 0.5141 | 18050 | 0.0001 | - | |
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| 0.5156 | 18100 | 0.0001 | - | |
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| 0.5170 | 18150 | 0.0001 | - | |
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| 0.5184 | 18200 | 0.0001 | - | |
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| 0.5198 | 18250 | 0.0001 | - | |
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| 0.5212 | 18300 | 0.0001 | - | |
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| 0.5227 | 18350 | 0.0001 | - | |
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| 0.5241 | 18400 | 0.0001 | - | |
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| 0.5255 | 18450 | 0.0001 | - | |
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| 0.5269 | 18500 | 0.0001 | - | |
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| 0.5284 | 18550 | 0.0001 | - | |
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| 0.5312 | 18650 | 0.0001 | - | |
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| 0.5326 | 18700 | 0.0001 | - | |
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| 0.5341 | 18750 | 0.0001 | - | |
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| 0.5355 | 18800 | 0.0001 | - | |
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| 0.5369 | 18850 | 0.0001 | - | |
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| 0.5383 | 18900 | 0.0001 | - | |
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| 0.5398 | 18950 | 0.0001 | - | |
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| 0.5412 | 19000 | 0.0001 | - | |
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| 0.5426 | 19050 | 0.0001 | - | |
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| 0.5440 | 19100 | 0.0001 | - | |
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| 0.5455 | 19150 | 0.0001 | - | |
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| 0.5469 | 19200 | 0.0001 | - | |
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| 0.5483 | 19250 | 0.0001 | - | |
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| 0.5497 | 19300 | 0.0001 | - | |
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| 0.5512 | 19350 | 0.0001 | - | |
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| 0.5526 | 19400 | 0.0001 | - | |
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| 0.5540 | 19450 | 0.0 | - | |
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| 0.5554 | 19500 | 0.0001 | - | |
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| 0.5569 | 19550 | 0.0001 | - | |
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| 0.5583 | 19600 | 0.0001 | - | |
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| 0.5597 | 19650 | 0.0001 | - | |
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| 0.5611 | 19700 | 0.0001 | - | |
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| 0.5625 | 19750 | 0.0001 | - | |
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| 0.5640 | 19800 | 0.0001 | - | |
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| 0.5654 | 19850 | 0.0001 | - | |
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| 0.5668 | 19900 | 0.0001 | - | |
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| 0.5682 | 19950 | 0.0001 | - | |
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| 0.5697 | 20000 | 0.0001 | - | |
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| 0.5711 | 20050 | 0.0001 | - | |
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| 0.5725 | 20100 | 0.0001 | - | |
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| 0.5739 | 20150 | 0.0001 | - | |
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| 0.5754 | 20200 | 0.0 | - | |
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| 0.5768 | 20250 | 0.0001 | - | |
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| 0.5782 | 20300 | 0.0001 | - | |
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| 0.5796 | 20350 | 0.0 | - | |
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| 0.5811 | 20400 | 0.0001 | - | |
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| 0.5825 | 20450 | 0.0001 | - | |
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| 0.5839 | 20500 | 0.0001 | - | |
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| 0.5853 | 20550 | 0.0001 | - | |
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| 0.5868 | 20600 | 0.0001 | - | |
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| 0.5882 | 20650 | 0.0001 | - | |
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| 0.5896 | 20700 | 0.0001 | - | |
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| 0.5910 | 20750 | 0.0001 | - | |
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| 0.5925 | 20800 | 0.0001 | - | |
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| 0.5939 | 20850 | 0.0001 | - | |
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| 0.5953 | 20900 | 0.0001 | - | |
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| 0.5967 | 20950 | 0.0001 | - | |
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| 0.5982 | 21000 | 0.0 | - | |
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| 0.5996 | 21050 | 0.0001 | - | |
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| 0.6010 | 21100 | 0.0001 | - | |
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| 0.6024 | 21150 | 0.0001 | - | |
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| 0.6039 | 21200 | 0.0001 | - | |
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| 0.6053 | 21250 | 0.0002 | - | |
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| 0.6067 | 21300 | 0.0001 | - | |
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| 0.6081 | 21350 | 0.0001 | - | |
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| 0.6095 | 21400 | 0.0001 | - | |
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| 0.6110 | 21450 | 0.0001 | - | |
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| 0.6124 | 21500 | 0.0001 | - | |
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| 0.6138 | 21550 | 0.0001 | - | |
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| 0.6152 | 21600 | 0.0001 | - | |
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| 0.6167 | 21650 | 0.0001 | - | |
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| 0.6181 | 21700 | 0.0001 | - | |
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| 0.6195 | 21750 | 0.0001 | - | |
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| 0.6209 | 21800 | 0.0001 | - | |
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| 0.6224 | 21850 | 0.0 | - | |
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| 0.6238 | 21900 | 0.0001 | - | |
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| 0.6252 | 21950 | 0.0001 | - | |
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| 0.6266 | 22000 | 0.0001 | - | |
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| 0.6281 | 22050 | 0.0001 | - | |
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| 0.6295 | 22100 | 0.0001 | - | |
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| 0.6309 | 22150 | 0.0001 | - | |
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| 0.6323 | 22200 | 0.0001 | - | |
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| 0.6338 | 22250 | 0.0001 | - | |
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| 0.6352 | 22300 | 0.0001 | - | |
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| 0.6366 | 22350 | 0.0001 | - | |
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| 0.6380 | 22400 | 0.0001 | - | |
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| 0.6395 | 22450 | 0.0001 | - | |
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| 0.6409 | 22500 | 0.0001 | - | |
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| 0.6423 | 22550 | 0.0001 | - | |
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| 0.6437 | 22600 | 0.0001 | - | |
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| 0.6452 | 22650 | 0.0001 | - | |
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| 0.6466 | 22700 | 0.0001 | - | |
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| 0.6480 | 22750 | 0.0001 | - | |
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| 0.6494 | 22800 | 0.0001 | - | |
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| 0.6508 | 22850 | 0.0001 | - | |
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| 0.6523 | 22900 | 0.0 | - | |
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| 0.6537 | 22950 | 0.0001 | - | |
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| 0.6551 | 23000 | 0.0001 | - | |
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| 0.6565 | 23050 | 0.0001 | - | |
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| 0.6580 | 23100 | 0.0001 | - | |
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| 0.6594 | 23150 | 0.0001 | - | |
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| 0.6608 | 23200 | 0.0001 | - | |
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| 0.6622 | 23250 | 0.0001 | - | |
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| 0.6637 | 23300 | 0.0 | - | |
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| 0.6651 | 23350 | 0.0001 | - | |
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| 0.6665 | 23400 | 0.0001 | - | |
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| 0.6679 | 23450 | 0.0001 | - | |
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| 0.6694 | 23500 | 0.0 | - | |
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| 0.6708 | 23550 | 0.0001 | - | |
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| 0.6722 | 23600 | 0.0 | - | |
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| 0.6736 | 23650 | 0.0001 | - | |
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| 0.6751 | 23700 | 0.0001 | - | |
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| 0.6765 | 23750 | 0.0 | - | |
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| 0.6779 | 23800 | 0.0001 | - | |
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| 0.6793 | 23850 | 0.0001 | - | |
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| 0.6808 | 23900 | 0.0001 | - | |
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| 0.6822 | 23950 | 0.0001 | - | |
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| 0.6836 | 24000 | 0.0 | - | |
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| 0.6850 | 24050 | 0.0001 | - | |
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| 0.6865 | 24100 | 0.0 | - | |
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| 0.6879 | 24150 | 0.0001 | - | |
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| 0.6893 | 24200 | 0.0001 | - | |
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| 0.6907 | 24250 | 0.0001 | - | |
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| 0.6921 | 24300 | 0.0001 | - | |
|
| 0.6936 | 24350 | 0.0 | - | |
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| 0.6950 | 24400 | 0.0001 | - | |
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| 0.6964 | 24450 | 0.0001 | - | |
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| 0.6978 | 24500 | 0.0001 | - | |
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| 0.6993 | 24550 | 0.0001 | - | |
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| 0.7007 | 24600 | 0.0 | - | |
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| 0.7021 | 24650 | 0.0 | - | |
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| 0.7035 | 24700 | 0.0001 | - | |
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| 0.7050 | 24750 | 0.0001 | - | |
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| 0.7064 | 24800 | 0.0001 | - | |
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| 0.7078 | 24850 | 0.0001 | - | |
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| 0.7092 | 24900 | 0.0001 | - | |
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| 0.7107 | 24950 | 0.0001 | - | |
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| 0.7121 | 25000 | 0.0001 | - | |
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| 0.7135 | 25050 | 0.0001 | - | |
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| 0.7149 | 25100 | 0.0001 | - | |
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| 0.7164 | 25150 | 0.0001 | - | |
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| 0.7178 | 25200 | 0.0001 | - | |
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| 0.7192 | 25250 | 0.0001 | - | |
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| 0.7206 | 25300 | 0.0001 | - | |
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| 0.7221 | 25350 | 0.0001 | - | |
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| 0.7235 | 25400 | 0.0001 | - | |
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| 0.7249 | 25450 | 0.0001 | - | |
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| 0.7263 | 25500 | 0.0001 | - | |
|
| 0.7278 | 25550 | 0.0 | - | |
|
| 0.7292 | 25600 | 0.0 | - | |
|
| 0.7306 | 25650 | 0.0 | - | |
|
| 0.7320 | 25700 | 0.0001 | - | |
|
| 0.7335 | 25750 | 0.0001 | - | |
|
| 0.7349 | 25800 | 0.0001 | - | |
|
| 0.7363 | 25850 | 0.0001 | - | |
|
| 0.7377 | 25900 | 0.0 | - | |
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| 0.7391 | 25950 | 0.0 | - | |
|
| 0.7406 | 26000 | 0.0001 | - | |
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| 0.7420 | 26050 | 0.0001 | - | |
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| 0.7434 | 26100 | 0.0 | - | |
|
| 0.7448 | 26150 | 0.0 | - | |
|
| 0.7463 | 26200 | 0.0001 | - | |
|
| 0.7477 | 26250 | 0.0 | - | |
|
| 0.7491 | 26300 | 0.0 | - | |
|
| 0.7505 | 26350 | 0.0 | - | |
|
| 0.7520 | 26400 | 0.0001 | - | |
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| 0.7534 | 26450 | 0.0 | - | |
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| 0.7548 | 26500 | 0.0001 | - | |
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| 0.7562 | 26550 | 0.0001 | - | |
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| 0.7577 | 26600 | 0.0001 | - | |
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| 0.7591 | 26650 | 0.0001 | - | |
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| 0.7605 | 26700 | 0.0 | - | |
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| 0.7619 | 26750 | 0.0001 | - | |
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| 0.7634 | 26800 | 0.0001 | - | |
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| 0.7648 | 26850 | 0.0001 | - | |
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| 0.7662 | 26900 | 0.0 | - | |
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| 0.7676 | 26950 | 0.0001 | - | |
|
| 0.7691 | 27000 | 0.0 | - | |
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| 0.7705 | 27050 | 0.0 | - | |
|
| 0.7719 | 27100 | 0.0001 | - | |
|
| 0.7733 | 27150 | 0.0 | - | |
|
| 0.7748 | 27200 | 0.0 | - | |
|
| 0.7762 | 27250 | 0.0001 | - | |
|
| 0.7776 | 27300 | 0.0001 | - | |
|
| 0.7790 | 27350 | 0.0001 | - | |
|
| 0.7804 | 27400 | 0.0001 | - | |
|
| 0.7819 | 27450 | 0.0 | - | |
|
| 0.7833 | 27500 | 0.0001 | - | |
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| 0.7847 | 27550 | 0.0 | - | |
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| 0.7861 | 27600 | 0.0 | - | |
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| 0.7876 | 27650 | 0.0001 | - | |
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| 0.7890 | 27700 | 0.0001 | - | |
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| 0.7904 | 27750 | 0.0 | - | |
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| 0.7918 | 27800 | 0.0001 | - | |
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| 0.7933 | 27850 | 0.0001 | - | |
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| 0.7947 | 27900 | 0.0 | - | |
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| 0.7961 | 27950 | 0.0 | - | |
|
| 0.7975 | 28000 | 0.0 | - | |
|
| 0.7990 | 28050 | 0.0001 | - | |
|
| 0.8004 | 28100 | 0.0 | - | |
|
| 0.8018 | 28150 | 0.0001 | - | |
|
| 0.8032 | 28200 | 0.0001 | - | |
|
| 0.8047 | 28250 | 0.0 | - | |
|
| 0.8061 | 28300 | 0.0 | - | |
|
| 0.8075 | 28350 | 0.0 | - | |
|
| 0.8089 | 28400 | 0.0001 | - | |
|
| 0.8104 | 28450 | 0.0 | - | |
|
| 0.8118 | 28500 | 0.0 | - | |
|
| 0.8132 | 28550 | 0.0 | - | |
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| 0.8146 | 28600 | 0.0 | - | |
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| 0.8161 | 28650 | 0.0 | - | |
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| 0.8175 | 28700 | 0.0 | - | |
|
| 0.8189 | 28750 | 0.0001 | - | |
|
| 0.8203 | 28800 | 0.0 | - | |
|
| 0.8218 | 28850 | 0.0 | - | |
|
| 0.8232 | 28900 | 0.0 | - | |
|
| 0.8246 | 28950 | 0.0001 | - | |
|
| 0.8260 | 29000 | 0.0 | - | |
|
| 0.8274 | 29050 | 0.0001 | - | |
|
| 0.8289 | 29100 | 0.0001 | - | |
|
| 0.8303 | 29150 | 0.0001 | - | |
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| 0.8317 | 29200 | 0.0001 | - | |
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| 0.8331 | 29250 | 0.0001 | - | |
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| 0.8346 | 29300 | 0.0001 | - | |
|
| 0.8360 | 29350 | 0.0 | - | |
|
| 0.8374 | 29400 | 0.0 | - | |
|
| 0.8388 | 29450 | 0.0001 | - | |
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| 0.8403 | 29500 | 0.0001 | - | |
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| 0.8417 | 29550 | 0.0001 | - | |
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| 0.8431 | 29600 | 0.0001 | - | |
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| 0.8445 | 29650 | 0.0001 | - | |
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| 0.8460 | 29700 | 0.0 | - | |
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| 0.8474 | 29750 | 0.0 | - | |
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| 0.8488 | 29800 | 0.0001 | - | |
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| 0.8502 | 29850 | 0.0001 | - | |
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| 0.8517 | 29900 | 0.0 | - | |
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| 0.8531 | 29950 | 0.0001 | - | |
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| 0.8545 | 30000 | 0.0001 | - | |
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| 0.8559 | 30050 | 0.0001 | - | |
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| 0.8574 | 30100 | 0.0001 | - | |
|
| 0.8588 | 30150 | 0.0 | - | |
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| 0.8602 | 30200 | 0.0 | - | |
|
| 0.8616 | 30250 | 0.0001 | - | |
|
| 0.8631 | 30300 | 0.0001 | - | |
|
| 0.8645 | 30350 | 0.0 | - | |
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| 0.8659 | 30400 | 0.0 | - | |
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| 0.8673 | 30450 | 0.0001 | - | |
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| 0.8687 | 30500 | 0.0 | - | |
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| 0.8702 | 30550 | 0.0 | - | |
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| 0.8716 | 30600 | 0.0 | - | |
|
| 0.8730 | 30650 | 0.0001 | - | |
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| 0.8744 | 30700 | 0.0 | - | |
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| 0.8759 | 30750 | 0.0 | - | |
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| 0.8773 | 30800 | 0.0001 | - | |
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| 0.8787 | 30850 | 0.0001 | - | |
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| 0.8801 | 30900 | 0.0 | - | |
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| 0.8816 | 30950 | 0.0 | - | |
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| 0.8830 | 31000 | 0.0 | - | |
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| 0.8844 | 31050 | 0.0001 | - | |
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| 0.8858 | 31100 | 0.0001 | - | |
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| 0.8873 | 31150 | 0.0001 | - | |
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| 0.8887 | 31200 | 0.0 | - | |
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| 0.8901 | 31250 | 0.0 | - | |
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| 0.8915 | 31300 | 0.0 | - | |
|
| 0.8930 | 31350 | 0.0001 | - | |
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| 0.8944 | 31400 | 0.0 | - | |
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| 0.8958 | 31450 | 0.0 | - | |
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| 0.8972 | 31500 | 0.0 | - | |
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| 0.8987 | 31550 | 0.0001 | - | |
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| 0.9001 | 31600 | 0.0 | - | |
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| 0.9015 | 31650 | 0.0 | - | |
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| 0.9029 | 31700 | 0.0001 | - | |
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| 0.9044 | 31750 | 0.0 | - | |
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| 0.9058 | 31800 | 0.0 | - | |
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| 0.9072 | 31850 | 0.0 | - | |
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| 0.9086 | 31900 | 0.0 | - | |
|
| 0.9100 | 31950 | 0.0001 | - | |
|
| 0.9115 | 32000 | 0.0001 | - | |
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| 0.9129 | 32050 | 0.0 | - | |
|
| 0.9143 | 32100 | 0.0 | - | |
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| 0.9157 | 32150 | 0.0 | - | |
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| 0.9172 | 32200 | 0.0 | - | |
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| 0.9186 | 32250 | 0.0 | - | |
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| 0.9200 | 32300 | 0.0 | - | |
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| 0.9214 | 32350 | 0.0 | - | |
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| 0.9229 | 32400 | 0.0 | - | |
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| 0.9243 | 32450 | 0.0 | - | |
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| 0.9257 | 32500 | 0.0 | - | |
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| 0.9271 | 32550 | 0.0 | - | |
|
| 0.9286 | 32600 | 0.0001 | - | |
|
| 0.9300 | 32650 | 0.0001 | - | |
|
| 0.9314 | 32700 | 0.0 | - | |
|
| 0.9328 | 32750 | 0.0001 | - | |
|
| 0.9343 | 32800 | 0.0 | - | |
|
| 0.9357 | 32850 | 0.0 | - | |
|
| 0.9371 | 32900 | 0.0 | - | |
|
| 0.9385 | 32950 | 0.0 | - | |
|
| 0.9400 | 33000 | 0.0 | - | |
|
| 0.9414 | 33050 | 0.0 | - | |
|
| 0.9428 | 33100 | 0.0 | - | |
|
| 0.9442 | 33150 | 0.0001 | - | |
|
| 0.9457 | 33200 | 0.0001 | - | |
|
| 0.9471 | 33250 | 0.0 | - | |
|
| 0.9485 | 33300 | 0.0 | - | |
|
| 0.9499 | 33350 | 0.0 | - | |
|
| 0.9514 | 33400 | 0.0 | - | |
|
| 0.9528 | 33450 | 0.0 | - | |
|
| 0.9542 | 33500 | 0.0001 | - | |
|
| 0.9556 | 33550 | 0.0 | - | |
|
| 0.9570 | 33600 | 0.0 | - | |
|
| 0.9585 | 33650 | 0.0 | - | |
|
| 0.9599 | 33700 | 0.0 | - | |
|
| 0.9613 | 33750 | 0.0001 | - | |
|
| 0.9627 | 33800 | 0.0 | - | |
|
| 0.9642 | 33850 | 0.0001 | - | |
|
| 0.9656 | 33900 | 0.0001 | - | |
|
| 0.9670 | 33950 | 0.0 | - | |
|
| 0.9684 | 34000 | 0.0 | - | |
|
| 0.9699 | 34050 | 0.0 | - | |
|
| 0.9713 | 34100 | 0.0001 | - | |
|
| 0.9727 | 34150 | 0.0001 | - | |
|
| 0.9741 | 34200 | 0.0 | - | |
|
| 0.9756 | 34250 | 0.0 | - | |
|
| 0.9770 | 34300 | 0.0 | - | |
|
| 0.9784 | 34350 | 0.0 | - | |
|
| 0.9798 | 34400 | 0.0 | - | |
|
| 0.9813 | 34450 | 0.0 | - | |
|
| 0.9827 | 34500 | 0.0 | - | |
|
| 0.9841 | 34550 | 0.0 | - | |
|
| 0.9855 | 34600 | 0.0 | - | |
|
| 0.9870 | 34650 | 0.0001 | - | |
|
| 0.9884 | 34700 | 0.0 | - | |
|
| 0.9898 | 34750 | 0.0 | - | |
|
| 0.9912 | 34800 | 0.0 | - | |
|
| 0.9927 | 34850 | 0.0001 | - | |
|
| 0.9941 | 34900 | 0.0 | - | |
|
| 0.9955 | 34950 | 0.0 | - | |
|
| 0.9969 | 35000 | 0.0001 | - | |
|
| 0.9983 | 35050 | 0.0 | - | |
|
| 0.9998 | 35100 | 0.0 | - | |
|
| **1.0** | **35108** | **-** | **0.03** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
### Framework Versions |
|
- Python: 3.11.9 |
|
- SetFit: 1.1.0.dev0 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.0+cu121 |
|
- Datasets: 2.21.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
```bibtex |
|
@article{https://doi.org/10.48550/arxiv.2209.11055, |
|
doi = {10.48550/ARXIV.2209.11055}, |
|
url = {https://arxiv.org/abs/2209.11055}, |
|
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
|
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
|
title = {Efficient Few-Shot Learning Without Prompts}, |
|
publisher = {arXiv}, |
|
year = {2022}, |
|
copyright = {Creative Commons Attribution 4.0 International} |
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} |
|
``` |
|
|
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