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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: hBERTv1_new_pretrain_w_init_48_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: 0.7752150832875709
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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_new_pretrain_w_init_48_qnli
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This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6240
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- Accuracy: 0.7752
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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 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.615 | 1.0 | 819 | 0.5774 | 0.7040 |
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| 0.5092 | 2.0 | 1638 | 0.4735 | 0.7818 |
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| 0.4554 | 3.0 | 2457 | 0.4670 | 0.7862 |
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| 0.4041 | 4.0 | 3276 | 0.4881 | 0.7807 |
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| 0.3567 | 5.0 | 4095 | 0.4527 | 0.7935 |
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| 0.3218 | 6.0 | 4914 | 0.5325 | 0.7820 |
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| 0.2864 | 7.0 | 5733 | 0.5454 | 0.7829 |
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| 0.2482 | 8.0 | 6552 | 0.5526 | 0.7701 |
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| 0.2146 | 9.0 | 7371 | 0.5932 | 0.7835 |
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| 0.1825 | 10.0 | 8190 | 0.6240 | 0.7752 |
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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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