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hBERTv2_data_aug_qqp
gokuls/hBERTv2_data_aug_qqp
hBERTv2_data_aug_qqp 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
- Mar 3, 2023
- Updated
- Mar 5, 2023
- SHA
- 553c7c0d
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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. -->
hBERTv2_data_aug_qqp
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2 on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.6782
- Accuracy: 0.6318
- F1: 0.0
- Combined Score: 0.3159
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: 256
- eval_batch_size: 256
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|---|---|---|---|---|---|---|
| 0.6528 | 1.0 | 29671 | 0.6782 | 0.6318 | 0.0 | 0.3159 |
| 0.6407 | 2.0 | 59342 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 3.0 | 89013 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 4.0 | 118684 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 5.0 | 148355 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 6.0 | 178026 | nan | 0.6318 | 0.0 | 0.3159 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- hybridbert
- Languages
- en
- Created
- Mar 3, 2023
- Updated
- Mar 5, 2023