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roBERTa
Daniel-xue/roBERTa
roBERTa is a text classification model from Daniel-xue. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Feb 15, 2024
- Updated
- Feb 16, 2024
- SHA
- baf3f8ca
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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. -->
roBERTa
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7581
- Accuracy: 0.8307
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: 3e-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
- lr_scheduler_warmup_steps: 15750
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5914 | 1.0 | 5250 | 0.9909 | 0.5628 |
| 0.5792 | 2.0 | 10500 | 0.9334 | 0.7436 |
| 0.5905 | 3.0 | 15750 | 0.5907 | 0.9454 |
| 0.6576 | 4.0 | 21000 | 1.2153 | 0.5174 |
| 0.5898 | 5.0 | 26250 | 0.6647 | 0.7933 |
| 0.5896 | 6.0 | 31500 | 0.7914 | 0.8132 |
| 0.6578 | 7.0 | 36750 | 0.6739 | 0.3249 |
| 0.5846 | 8.0 | 42000 | 0.6237 | 0.8294 |
| 0.549 | 9.0 | 47250 | 0.7103 | 0.8514 |
| 0.5497 | 10.0 | 52500 | 0.7581 | 0.8307 |
Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Feb 15, 2024
- Updated
- Feb 16, 2024