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HBERTv1_48_L6_H256_A4_massive
This model is a fine-tuned version of gokuls/HBERTv1_48_L6_H256_A4 on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 0.7233
- Accuracy: 0.8460
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 |
|---|---|---|---|---|
| 3.3254 | 1.0 | 180 | 2.3694 | 0.4638 |
| 1.8706 | 2.0 | 360 | 1.3999 | 0.6616 |
| 1.2107 | 3.0 | 540 | 1.0206 | 0.7378 |
| 0.8953 | 4.0 | 720 | 0.8675 | 0.7821 |
| 0.6964 | 5.0 | 900 | 0.7948 | 0.7973 |
| 0.5749 | 6.0 | 1080 | 0.7426 | 0.8165 |
| 0.4668 | 7.0 | 1260 | 0.7449 | 0.8180 |
| 0.3947 | 8.0 | 1440 | 0.7142 | 0.8283 |
| 0.3345 | 9.0 | 1620 | 0.7030 | 0.8406 |
| 0.2859 | 10.0 | 1800 | 0.7111 | 0.8411 |
| 0.2418 | 11.0 | 1980 | 0.7323 | 0.8392 |
| 0.2145 | 12.0 | 2160 | 0.7269 | 0.8392 |
| 0.1885 | 13.0 | 2340 | 0.7233 | 0.8460 |
| 0.17 | 14.0 | 2520 | 0.7294 | 0.8411 |
| 0.1579 | 15.0 | 2700 | 0.7331 | 0.8436 |
Framework versions
- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.0
