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roberta-base-RILE-v1_un_frozen
kghanlon/roberta-base-RILE-v1_un_frozen
roberta-base-RILE-v1_un_frozen is a text classification model from kghanlon. 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
- Dec 26, 2023
- Updated
- Dec 26, 2023
- SHA
- 83889968
Try a prompt
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-base-RILE-v1_un_frozen
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6949
- Accuracy: 0.7479
- Recall: 0.7479
- F1: 0.7476
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: 5e-06
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 |
|---|---|---|---|---|---|---|
| 0.6689 | 1.0 | 15490 | 0.6575 | 0.7284 | 0.7284 | 0.7278 |
| 0.6253 | 2.0 | 30980 | 0.6478 | 0.7369 | 0.7369 | 0.7378 |
| 0.5822 | 3.0 | 46470 | 0.6475 | 0.7441 | 0.7441 | 0.7437 |
| 0.5066 | 4.0 | 61960 | 0.6737 | 0.7472 | 0.7472 | 0.7462 |
| 0.4645 | 5.0 | 77450 | 0.6949 | 0.7479 | 0.7479 | 0.7476 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Dec 26, 2023
- Updated
- Dec 26, 2023