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README.md
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---
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tags:
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- generated_from_trainer
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datasets:
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- glue
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metrics:
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- accuracy
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- f1
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model-index:
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- name: hBERTv2_qqp
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: glue
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type: glue
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config: qqp
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split: validation
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args: qqp
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8760079149146673
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- name: F1
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type: f1
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value: 0.8322233006459385
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# hBERTv2_qqp
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This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2](https://huggingface.co/gokuls/bert_12_layer_model_v2) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4723
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- Accuracy: 0.8760
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- F1: 0.8322
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- Combined Score: 0.8541
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 256
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- eval_batch_size: 256
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- seed: 10
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- distributed_type: multi-GPU
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 50
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
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| 0.4179 | 1.0 | 1422 | 0.3830 | 0.8252 | 0.7916 | 0.8084 |
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| 0.2978 | 2.0 | 2844 | 0.3507 | 0.8357 | 0.7906 | 0.8131 |
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| 0.2318 | 3.0 | 4266 | 0.3129 | 0.8651 | 0.8160 | 0.8406 |
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| 0.1765 | 4.0 | 5688 | 0.3540 | 0.8700 | 0.8328 | 0.8514 |
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| 0.1305 | 5.0 | 7110 | 0.4276 | 0.8734 | 0.8267 | 0.8500 |
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| 0.1003 | 6.0 | 8532 | 0.4078 | 0.8748 | 0.8292 | 0.8520 |
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| 0.0788 | 7.0 | 9954 | 0.4069 | 0.8767 | 0.8345 | 0.8556 |
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| 0.0625 | 8.0 | 11376 | 0.4723 | 0.8760 | 0.8322 | 0.8541 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 1.14.0a0+410ce96
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- Datasets 2.10.1
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- Tokenizers 0.13.2
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