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roberta-base-RILE-v1_frozen_8
kghanlon/roberta-base-RILE-v1_frozen_8
roberta-base-RILE-v1_frozen_8 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
- a74ef95d
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_frozen_8
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6666
- Accuracy: 0.7314
- Recall: 0.7314
- F1: 0.7307
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 |
|---|---|---|---|---|---|---|
| 0.6992 | 1.0 | 15490 | 0.6917 | 0.7119 | 0.7119 | 0.7109 |
| 0.6809 | 2.0 | 30980 | 0.6737 | 0.7205 | 0.7205 | 0.7207 |
| 0.6645 | 3.0 | 46470 | 0.6644 | 0.7256 | 0.7256 | 0.7247 |
| 0.6248 | 4.0 | 61960 | 0.6664 | 0.7292 | 0.7292 | 0.7275 |
| 0.6124 | 5.0 | 77450 | 0.6666 | 0.7314 | 0.7314 | 0.7307 |
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