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hbertv2-Massive-intent_w_in
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new_wt_init on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 0.8617
- Accuracy: 0.8701
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 |
|---|---|---|---|---|
| 1.7408 | 1.0 | 180 | 0.8947 | 0.7600 |
| 0.7657 | 2.0 | 360 | 0.7246 | 0.8077 |
| 0.5442 | 3.0 | 540 | 0.7033 | 0.8259 |
| 0.3906 | 4.0 | 720 | 0.7175 | 0.8278 |
| 0.2839 | 5.0 | 900 | 0.6561 | 0.8465 |
| 0.2105 | 6.0 | 1080 | 0.6862 | 0.8485 |
| 0.1456 | 7.0 | 1260 | 0.7010 | 0.8574 |
| 0.1227 | 8.0 | 1440 | 0.7380 | 0.8524 |
| 0.0859 | 9.0 | 1620 | 0.8052 | 0.8539 |
| 0.0584 | 10.0 | 1800 | 0.8228 | 0.8593 |
| 0.0484 | 11.0 | 1980 | 0.8198 | 0.8588 |
| 0.028 | 12.0 | 2160 | 0.8731 | 0.8569 |
| 0.0202 | 13.0 | 2340 | 0.8640 | 0.8647 |
| 0.0103 | 14.0 | 2520 | 0.8691 | 0.8637 |
| 0.0059 | 15.0 | 2700 | 0.8617 | 0.8701 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
