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CS505-Classifier-T4_predictLabel_a1_v9
ThuyNT03/CS505-Classifier-T4_predictLabel_a1_v9
CS505-Classifier-T4_predictLabel_a1_v9 is a text classification model from ThuyNT03. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Mar 10, 2024
- Updated
- Mar 10, 2024
- SHA
- 35470f4e
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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. -->
CS505-Classifier-T4_predictLabel_a1_v9
This model is a fine-tuned version of vinai/phobert-base-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0355
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-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 70
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.98 | 48 | 0.6966 |
| No log | 1.96 | 96 | 0.3265 |
| No log | 2.94 | 144 | 0.2746 |
| No log | 3.92 | 192 | 0.1899 |
| No log | 4.9 | 240 | 0.1671 |
| No log | 5.88 | 288 | 0.1193 |
| No log | 6.86 | 336 | 0.1259 |
| No log | 7.84 | 384 | 0.0737 |
| No log | 8.82 | 432 | 0.0461 |
| No log | 9.8 | 480 | 0.0490 |
| 0.3023 | 10.78 | 528 | 0.0293 |
| 0.3023 | 11.76 | 576 | 0.0324 |
| 0.3023 | 12.73 | 624 | 0.0355 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Mar 10, 2024
- Updated
- Mar 10, 2024