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clinic_oos_finetuned_roberta_model
MRUNLLM/clinic_oos_finetuned_roberta_model
clinic_oos_finetuned_roberta_model is a text classification model from MRUNLLM. 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
- Oct 27, 2023
- Updated
- Oct 27, 2023
- SHA
- 3c8940e2
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Base models
Datasets
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. -->
output
This model is a fine-tuned version of roberta-large on the clinc_oos dataset. It achieves the following results on the evaluation set:
- Loss: 0.1692
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3719 | 1.0 | 954 | 0.3159 |
| 0.0131 | 2.0 | 1908 | 0.1692 |
| 0.0149 | 3.0 | 2862 | 0.1947 |
| 0.0179 | 4.0 | 3816 | 0.1907 |
| 0.04 | 5.0 | 4770 | 0.1877 |
| 0.001 | 6.0 | 5724 | 0.1908 |
| 0.0473 | 7.0 | 6678 | 0.1961 |
| 0.0007 | 8.0 | 7632 | 0.1960 |
| 0.0415 | 9.0 | 8586 | 0.1945 |
| 0.0005 | 10.0 | 9540 | 0.1971 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Oct 27, 2023
- Updated
- Oct 27, 2023