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HBERTv1_48_L12_H256_A4_massive
This model is a fine-tuned version of gokuls/HBERTv1_48_L12_H256_A4 on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 1.1699
- Accuracy: 0.7339
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: 64
- eval_batch_size: 64
- seed: 33
- distributed_type: multi-GPU
- 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 | Accuracy |
|---|---|---|---|---|
| 3.7238 | 1.0 | 180 | 3.4052 | 0.1382 |
| 3.1325 | 2.0 | 360 | 2.8875 | 0.2022 |
| 2.7162 | 3.0 | 540 | 2.5311 | 0.3030 |
| 2.4123 | 4.0 | 720 | 2.3315 | 0.3576 |
| 2.1258 | 5.0 | 900 | 2.0547 | 0.4186 |
| 1.8697 | 6.0 | 1080 | 1.8215 | 0.4889 |
| 1.6446 | 7.0 | 1260 | 1.6681 | 0.5421 |
| 1.4509 | 8.0 | 1440 | 1.5200 | 0.5853 |
| 1.2995 | 9.0 | 1620 | 1.4177 | 0.6188 |
| 1.1585 | 10.0 | 1800 | 1.3337 | 0.6557 |
| 1.0714 | 11.0 | 1980 | 1.2620 | 0.7059 |
| 0.9816 | 12.0 | 2160 | 1.2374 | 0.7147 |
| 0.9053 | 13.0 | 2340 | 1.1849 | 0.7290 |
| 0.8582 | 14.0 | 2520 | 1.1721 | 0.7324 |
| 0.8253 | 15.0 | 2700 | 1.1699 | 0.7339 |
Framework versions
- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.0
