bert-tiny-Massive-intent-KD-BERT
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 0.8380
- Accuracy: 0.8534
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: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 5.83 | 1.0 | 720 | 4.8826 | 0.3050 |
| 4.7602 | 2.0 | 1440 | 3.9904 | 0.4191 |
| 4.0301 | 3.0 | 2160 | 3.3806 | 0.5032 |
| 3.4797 | 4.0 | 2880 | 2.9065 | 0.5967 |
| 3.0352 | 5.0 | 3600 | 2.5389 | 0.6596 |
| 2.6787 | 6.0 | 4320 | 2.2342 | 0.7044 |
| 2.3644 | 7.0 | 5040 | 1.9873 | 0.7354 |
| 2.1145 | 8.0 | 5760 | 1.7928 | 0.7462 |
| 1.896 | 9.0 | 6480 | 1.6293 | 0.7644 |
| 1.7138 | 10.0 | 7200 | 1.5062 | 0.7752 |
| 1.5625 | 11.0 | 7920 | 1.3923 | 0.7885 |
| 1.4229 | 12.0 | 8640 | 1.3092 | 0.7978 |
| 1.308 | 13.0 | 9360 | 1.2364 | 0.8018 |
| 1.201 | 14.0 | 10080 | 1.1759 | 0.8155 |
| 1.1187 | 15.0 | 10800 | 1.1322 | 0.8214 |
| 1.0384 | 16.0 | 11520 | 1.0990 | 0.8234 |
| 0.976 | 17.0 | 12240 | 1.0615 | 0.8308 |
| 0.9163 | 18.0 | 12960 | 1.0377 | 0.8328 |
| 0.8611 | 19.0 | 13680 | 1.0054 | 0.8337 |
| 0.812 | 20.0 | 14400 | 0.9926 | 0.8367 |
| 0.7721 | 21.0 | 15120 | 0.9712 | 0.8382 |
| 0.7393 | 22.0 | 15840 | 0.9586 | 0.8357 |
| 0.7059 | 23.0 | 16560 | 0.9428 | 0.8372 |
| 0.6741 | 24.0 | 17280 | 0.9377 | 0.8396 |
| 0.6552 | 25.0 | 18000 | 0.9229 | 0.8377 |
| 0.627 | 26.0 | 18720 | 0.9100 | 0.8416 |
| 0.5972 | 27.0 | 19440 | 0.9028 | 0.8416 |
| 0.5784 | 28.0 | 20160 | 0.8996 | 0.8406 |
| 0.5595 | 29.0 | 20880 | 0.8833 | 0.8451 |
| 0.5438 | 30.0 | 21600 | 0.8772 | 0.8475 |
| 0.5218 | 31.0 | 22320 | 0.8758 | 0.8451 |
| 0.509 | 32.0 | 23040 | 0.8728 | 0.8480 |
| 0.4893 | 33.0 | 23760 | 0.8640 | 0.8480 |
| 0.4948 | 34.0 | 24480 | 0.8541 | 0.8475 |
| 0.4722 | 35.0 | 25200 | 0.8595 | 0.8495 |
| 0.468 | 36.0 | 25920 | 0.8488 | 0.8495 |
| 0.4517 | 37.0 | 26640 | 0.8460 | 0.8505 |
| 0.4462 | 38.0 | 27360 | 0.8450 | 0.8485 |
| 0.4396 | 39.0 | 28080 | 0.8422 | 0.8490 |
| 0.427 | 40.0 | 28800 | 0.8380 | 0.8534 |
| 0.4287 | 41.0 | 29520 | 0.8385 | 0.8480 |
| 0.4222 | 42.0 | 30240 | 0.8319 | 0.8510 |
| 0.421 | 43.0 | 30960 | 0.8296 | 0.8510 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1