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insuff_supported_arguments
argmin-2024-bumblebearhug/insuff_supported_arguments
insuff_supported_arguments is a text classification model from argmin-2024-bumblebearhug. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Mar 1, 2024
- Updated
- Mar 1, 2024
- SHA
- a08170fe
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
insuff_supported_arguments
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0
- Negative: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13620.0}
- Positive: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6960.0}
- Accuracy: 1.0
- Macro avg: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0}
- Weighted avg: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0}
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Negative | Positive | Accuracy | Macro avg | Weighted avg |
|---|---|---|---|---|---|---|---|---|
| 0.0004 | 1.0 | 9255 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13620.0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6960.0} | 1.0 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0} |
| 0.0 | 2.0 | 18510 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13620.0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6960.0} | 1.0 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0} |
| 0.0 | 3.0 | 27765 | 0.0 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13620.0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6960.0} | 1.0 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0} |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 1, 2024
- Updated
- Mar 1, 2024