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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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model-index:
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- name: hBERTv2_qnli
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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: qnli
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split: validation
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args: qnli
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.4946000366099213
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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_qnli
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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: 0.6931
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- Accuracy: 0.4946
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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 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.6968 | 1.0 | 410 | 0.6952 | 0.5054 |
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| 0.6943 | 2.0 | 820 | 0.6932 | 0.4946 |
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| 0.6937 | 3.0 | 1230 | 0.6933 | 0.5054 |
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| 0.6934 | 4.0 | 1640 | 0.6931 | 0.5054 |
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| 0.6934 | 5.0 | 2050 | 0.6931 | 0.5054 |
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| 0.6933 | 6.0 | 2460 | 0.6930 | 0.5054 |
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| 0.6933 | 7.0 | 2870 | 0.6931 | 0.5054 |
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| 0.6932 | 8.0 | 3280 | 0.6930 | 0.5054 |
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| 0.6932 | 9.0 | 3690 | 0.6934 | 0.4946 |
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| 0.6932 | 10.0 | 4100 | 0.6930 | 0.5054 |
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| 0.6932 | 11.0 | 4510 | 0.6931 | 0.4946 |
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| 0.6933 | 12.0 | 4920 | 0.6934 | 0.4946 |
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| 0.6932 | 13.0 | 5330 | 0.6931 | 0.4946 |
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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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