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hBERTv1_qnli
gokuls/hBERTv1_qnli
hBERTv1_qnli 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 1, 2023
- Updated
- Mar 2, 2023
- SHA
- b1261c8a
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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_qnli
This model is a fine-tuned version of gokuls/bert_12_layer_model_v1 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.4198
- Accuracy: 0.8113
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 |
|---|---|---|---|---|
| 0.6667 | 1.0 | 410 | 0.5955 | 0.6874 |
| 0.4998 | 2.0 | 820 | 0.4486 | 0.7948 |
| 0.3985 | 3.0 | 1230 | 0.4198 | 0.8113 |
| 0.3106 | 4.0 | 1640 | 0.4841 | 0.7866 |
| 0.2286 | 5.0 | 2050 | 0.5340 | 0.7906 |
| 0.1662 | 6.0 | 2460 | 0.6282 | 0.7728 |
| 0.1237 | 7.0 | 2870 | 0.6678 | 0.7752 |
| 0.0945 | 8.0 | 3280 | 0.7668 | 0.7752 |
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 1, 2023
- Updated
- Mar 2, 2023