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hbertv1-massive-logit_KD-mini
gokuls/hbertv1-massive-logit_KD-mini
hbertv1-massive-logit_KD-mini is a text classification model from gokuls. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- hybridbert
- Access
- Public
- Created
- Nov 26, 2023
- Updated
- Nov 26, 2023
- SHA
- 0366f640
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Base models
Datasets
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. -->
hbertv1-massive-logit_KD-mini
This model is a fine-tuned version of gokuls/model_v1_complete_training_wt_init_48_mini_freeze_new on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 0.4640
- Accuracy: 0.8598
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: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.5547 | 1.0 | 180 | 2.3028 | 0.4481 |
| 1.9374 | 2.0 | 360 | 1.2686 | 0.6513 |
| 1.2845 | 3.0 | 540 | 0.9328 | 0.7324 |
| 0.9981 | 4.0 | 720 | 0.7684 | 0.7836 |
| 0.8273 | 5.0 | 900 | 0.6834 | 0.7998 |
| 0.7068 | 6.0 | 1080 | 0.6369 | 0.8062 |
| 0.6043 | 7.0 | 1260 | 0.5804 | 0.8205 |
| 0.535 | 8.0 | 1440 | 0.5475 | 0.8396 |
| 0.4763 | 9.0 | 1620 | 0.5247 | 0.8396 |
| 0.4245 | 10.0 | 1800 | 0.5122 | 0.8470 |
| 0.3794 | 11.0 | 1980 | 0.5038 | 0.8460 |
| 0.3424 | 12.0 | 2160 | 0.5057 | 0.8465 |
| 0.3194 | 13.0 | 2340 | 0.4977 | 0.8485 |
| 0.2897 | 14.0 | 2520 | 0.4973 | 0.8534 |
| 0.2688 | 15.0 | 2700 | 0.4714 | 0.8574 |
| 0.255 | 16.0 | 2880 | 0.4763 | 0.8480 |
| 0.2401 | 17.0 | 3060 | 0.4856 | 0.8510 |
| 0.2286 | 18.0 | 3240 | 0.4713 | 0.8578 |
| 0.2138 | 19.0 | 3420 | 0.4753 | 0.8500 |
| 0.2022 | 20.0 | 3600 | 0.4641 | 0.8544 |
| 0.1937 | 21.0 | 3780 | 0.4640 | 0.8598 |
| 0.1802 | 22.0 | 3960 | 0.4788 | 0.8505 |
| 0.1719 | 23.0 | 4140 | 0.4520 | 0.8593 |
| 0.17 | 24.0 | 4320 | 0.4703 | 0.8564 |
| 0.159 | 25.0 | 4500 | 0.4620 | 0.8554 |
| 0.1566 | 26.0 | 4680 | 0.4825 | 0.8549 |
Framework versions
- Transformers 4.35.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- hybridbert
- Created
- Nov 26, 2023
- Updated
- Nov 26, 2023