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hBERTv1_new_pretrain_w_init_48_mrpc
gokuls/hBERTv1_new_pretrain_w_init_48_mrpc
hBERTv1_new_pretrain_w_init_48_mrpc 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
- Jun 6, 2023
- Updated
- Jun 6, 2023
- SHA
- f0668543
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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_new_pretrain_w_init_48_mrpc
This model is a fine-tuned version of gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48 on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 0.6229
- Accuracy: 0.6838
- F1: 0.8122
- Combined Score: 0.7480
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: 4e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- 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 | F1 | Combined Score |
|---|---|---|---|---|---|---|
| 0.6607 | 1.0 | 29 | 0.6262 | 0.6838 | 0.8122 | 0.7480 |
| 0.6421 | 2.0 | 58 | 0.6368 | 0.6838 | 0.8122 | 0.7480 |
| 0.6411 | 3.0 | 87 | 0.6258 | 0.6838 | 0.8122 | 0.7480 |
| 0.6406 | 4.0 | 116 | 0.6422 | 0.6838 | 0.8122 | 0.7480 |
| 0.6364 | 5.0 | 145 | 0.6263 | 0.6838 | 0.8122 | 0.7480 |
| 0.6322 | 6.0 | 174 | 0.6253 | 0.6838 | 0.8122 | 0.7480 |
| 0.6398 | 7.0 | 203 | 0.6289 | 0.6838 | 0.8122 | 0.7480 |
| 0.6363 | 8.0 | 232 | 0.6267 | 0.6838 | 0.8122 | 0.7480 |
| 0.6374 | 9.0 | 261 | 0.6375 | 0.6838 | 0.8122 | 0.7480 |
| 0.6374 | 10.0 | 290 | 0.6248 | 0.6838 | 0.8122 | 0.7480 |
| 0.638 | 11.0 | 319 | 0.6262 | 0.6838 | 0.8122 | 0.7480 |
| 0.6353 | 12.0 | 348 | 0.6236 | 0.6838 | 0.8122 | 0.7480 |
| 0.6338 | 13.0 | 377 | 0.6263 | 0.6838 | 0.8122 | 0.7480 |
| 0.637 | 14.0 | 406 | 0.6250 | 0.6838 | 0.8122 | 0.7480 |
| 0.6375 | 15.0 | 435 | 0.6229 | 0.6838 | 0.8122 | 0.7480 |
| 0.7037 | 16.0 | 464 | 0.6438 | 0.6838 | 0.8122 | 0.7480 |
| 0.6198 | 17.0 | 493 | 0.6242 | 0.6961 | 0.8038 | 0.7499 |
| 0.5847 | 18.0 | 522 | 0.6260 | 0.6740 | 0.7742 | 0.7241 |
| 0.4983 | 19.0 | 551 | 0.7174 | 0.7034 | 0.8158 | 0.7596 |
| 0.4245 | 20.0 | 580 | 0.7737 | 0.6789 | 0.7828 | 0.7308 |
Framework versions
- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- hybridbert
- Languages
- en
- Created
- Jun 6, 2023
- Updated
- Jun 6, 2023