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debert-imeocap
sakren/debert-imeocap
debert-imeocap is a text classification model from sakren. 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
- deberta-v2
- Access
- Public
- Created
- May 15, 2024
- Updated
- May 15, 2024
- SHA
- 5206cd4b
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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. -->
debert-imeocap
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8660
- F1: 0.6185
- Precision: 0.6337
- Recall: 0.6154
- Accuracy: 0.6154
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.4637 | 1.0 | 74 | 1.3864 | 0.6129 | 0.6262 | 0.6115 | 0.6115 |
| 0.3815 | 2.0 | 148 | 1.3801 | 0.6193 | 0.6348 | 0.6173 | 0.6173 |
| 0.3363 | 3.0 | 222 | 1.6944 | 0.6077 | 0.6297 | 0.6077 | 0.6077 |
| 0.31 | 4.0 | 296 | 1.6945 | 0.5995 | 0.6285 | 0.5942 | 0.5942 |
| 0.2885 | 5.0 | 370 | 1.5945 | 0.6218 | 0.6306 | 0.6192 | 0.6192 |
| 0.2594 | 6.0 | 444 | 1.7662 | 0.6279 | 0.6396 | 0.625 | 0.625 |
| 0.2319 | 7.0 | 518 | 1.7093 | 0.6210 | 0.6321 | 0.6173 | 0.6173 |
| 0.2306 | 8.0 | 592 | 1.8068 | 0.6279 | 0.6341 | 0.6288 | 0.6288 |
| 0.2167 | 9.0 | 666 | 1.7306 | 0.6376 | 0.6444 | 0.6346 | 0.6346 |
| 0.2158 | 10.0 | 740 | 1.8745 | 0.6262 | 0.6318 | 0.6269 | 0.6269 |
| 0.222 | 11.0 | 814 | 1.8323 | 0.6200 | 0.6348 | 0.6173 | 0.6173 |
| 0.2152 | 12.0 | 888 | 1.8576 | 0.6246 | 0.6363 | 0.6212 | 0.6212 |
| 0.226 | 13.0 | 962 | 1.8880 | 0.6343 | 0.6411 | 0.6308 | 0.6308 |
| 0.2097 | 14.0 | 1036 | 1.8884 | 0.6152 | 0.6326 | 0.6115 | 0.6115 |
| 0.2192 | 15.0 | 1110 | 1.8660 | 0.6185 | 0.6337 | 0.6154 | 0.6154 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- May 15, 2024
- Updated
- May 15, 2024