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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: hBERTv1_mrpc
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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: mrpc
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split: validation
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args: mrpc
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.6838235294117647
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- name: F1
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type: f1
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value: 0.7809847198641766
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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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# hBERTv1_mrpc
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This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1](https://huggingface.co/gokuls/bert_12_layer_model_v1) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9708
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- Accuracy: 0.6838
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- F1: 0.7810
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- Combined Score: 0.7324
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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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| 0.6536 | 1.0 | 15 | 0.6243 | 0.6838 | 0.8122 | 0.7480 |
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| 0.6275 | 2.0 | 30 | 0.6174 | 0.7010 | 0.8117 | 0.7564 |
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| 0.6129 | 3.0 | 45 | 0.6089 | 0.6961 | 0.8182 | 0.7571 |
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| 0.6087 | 4.0 | 60 | 0.6062 | 0.6887 | 0.8130 | 0.7508 |
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| 0.5939 | 5.0 | 75 | 0.6104 | 0.6863 | 0.7935 | 0.7399 |
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| 0.5707 | 6.0 | 90 | 0.6184 | 0.7083 | 0.8183 | 0.7633 |
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| 0.5426 | 7.0 | 105 | 0.6051 | 0.6863 | 0.8000 | 0.7431 |
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| 0.4819 | 8.0 | 120 | 0.6560 | 0.6936 | 0.8019 | 0.7478 |
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| 0.4279 | 9.0 | 135 | 0.6673 | 0.6887 | 0.7678 | 0.7283 |
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| 0.3374 | 10.0 | 150 | 0.8092 | 0.6863 | 0.7902 | 0.7382 |
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| 0.2789 | 11.0 | 165 | 0.9342 | 0.6887 | 0.7935 | 0.7411 |
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| 0.2216 | 12.0 | 180 | 0.9708 | 0.6838 | 0.7810 | 0.7324 |
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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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