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hbertv1-massive-logit_KD-tiny
gokuls/hbertv1-massive-logit_KD-tiny
hbertv1-massive-logit_KD-tiny 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
- e74e456d
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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-tiny
This model is a fine-tuned version of gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 0.5468
- Accuracy: 0.8465
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 |
|---|---|---|---|---|
| 4.0471 | 1.0 | 180 | 3.2580 | 0.2258 |
| 2.9727 | 2.0 | 360 | 2.3478 | 0.3778 |
| 2.3183 | 3.0 | 540 | 1.8643 | 0.5081 |
| 1.9162 | 4.0 | 720 | 1.5331 | 0.6375 |
| 1.6284 | 5.0 | 900 | 1.3079 | 0.6931 |
| 1.4163 | 6.0 | 1080 | 1.1495 | 0.7241 |
| 1.263 | 7.0 | 1260 | 1.0287 | 0.7437 |
| 1.1491 | 8.0 | 1440 | 0.9566 | 0.7575 |
| 1.0652 | 9.0 | 1620 | 0.8881 | 0.7644 |
| 0.9661 | 10.0 | 1800 | 0.8426 | 0.7801 |
| 0.9077 | 11.0 | 1980 | 0.7980 | 0.7796 |
| 0.8466 | 12.0 | 2160 | 0.7675 | 0.7919 |
| 0.7996 | 13.0 | 2340 | 0.7422 | 0.7934 |
| 0.7605 | 14.0 | 2520 | 0.7323 | 0.7954 |
| 0.7156 | 15.0 | 2700 | 0.6864 | 0.8067 |
| 0.6867 | 16.0 | 2880 | 0.6730 | 0.8131 |
| 0.6493 | 17.0 | 3060 | 0.6548 | 0.8160 |
| 0.6245 | 18.0 | 3240 | 0.6495 | 0.8136 |
| 0.6038 | 19.0 | 3420 | 0.6282 | 0.8224 |
| 0.57 | 20.0 | 3600 | 0.6123 | 0.8224 |
| 0.556 | 21.0 | 3780 | 0.6020 | 0.8308 |
| 0.5334 | 22.0 | 3960 | 0.5943 | 0.8298 |
| 0.5101 | 23.0 | 4140 | 0.5778 | 0.8323 |
| 0.4948 | 24.0 | 4320 | 0.5740 | 0.8337 |
| 0.4824 | 25.0 | 4500 | 0.5772 | 0.8337 |
| 0.4728 | 26.0 | 4680 | 0.5712 | 0.8342 |
| 0.4596 | 27.0 | 4860 | 0.5691 | 0.8337 |
| 0.4436 | 28.0 | 5040 | 0.5670 | 0.8396 |
| 0.4367 | 29.0 | 5220 | 0.5542 | 0.8367 |
| 0.4249 | 30.0 | 5400 | 0.5512 | 0.8406 |
| 0.4117 | 31.0 | 5580 | 0.5450 | 0.8387 |
| 0.4051 | 32.0 | 5760 | 0.5468 | 0.8465 |
| 0.4 | 33.0 | 5940 | 0.5464 | 0.8401 |
| 0.3939 | 34.0 | 6120 | 0.5451 | 0.8446 |
| 0.3801 | 35.0 | 6300 | 0.5387 | 0.8441 |
| 0.3708 | 36.0 | 6480 | 0.5353 | 0.8421 |
| 0.3686 | 37.0 | 6660 | 0.5320 | 0.8455 |
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