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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_new_pretrain_48_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: 0.8300766757358398
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- name: F1
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type: f1
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value: 0.7659443990188063
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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_new_pretrain_48_qqp
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This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_48) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4908
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- Accuracy: 0.8301
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- F1: 0.7659
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- Combined Score: 0.7980
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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: 4e-05
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- train_batch_size: 128
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- eval_batch_size: 128
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
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| 0.5044 | 1.0 | 2843 | 0.4468 | 0.7865 | 0.6961 | 0.7413 |
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| 0.4102 | 2.0 | 5686 | 0.4359 | 0.7992 | 0.6935 | 0.7464 |
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| 0.3553 | 3.0 | 8529 | 0.4127 | 0.8080 | 0.7105 | 0.7592 |
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| 0.3122 | 4.0 | 11372 | 0.4029 | 0.8216 | 0.7562 | 0.7889 |
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| 0.2756 | 5.0 | 14215 | 0.4481 | 0.8228 | 0.7518 | 0.7873 |
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| 0.2479 | 6.0 | 17058 | 0.4778 | 0.8268 | 0.7633 | 0.7951 |
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| 0.223 | 7.0 | 19901 | 0.4425 | 0.8158 | 0.7721 | 0.7939 |
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| 0.2028 | 8.0 | 22744 | 0.4705 | 0.8267 | 0.7686 | 0.7977 |
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| 0.183 | 9.0 | 25587 | 0.4908 | 0.8301 | 0.7659 | 0.7980 |
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### Framework versions
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- Transformers 4.29.2
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- Pytorch 1.14.0a0+410ce96
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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