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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__wnli
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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: wnli
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split: validation
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args: wnli
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.43661971830985913
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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__wnli
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This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_wt_init](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_wt_init) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7009
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- Accuracy: 0.4366
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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: 0.0005
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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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| 12.3688 | 1.0 | 5 | 6.2236 | 0.5634 |
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| 3.5093 | 2.0 | 10 | 0.7491 | 0.4366 |
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| 1.9112 | 3.0 | 15 | 2.5146 | 0.5634 |
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| 1.4995 | 4.0 | 20 | 1.8104 | 0.4366 |
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| 1.3047 | 5.0 | 25 | 0.6936 | 0.5634 |
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| 1.4685 | 6.0 | 30 | 0.7440 | 0.5634 |
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| 0.924 | 7.0 | 35 | 1.1066 | 0.4366 |
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| 0.8423 | 8.0 | 40 | 0.8221 | 0.4366 |
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| 0.8166 | 9.0 | 45 | 0.6855 | 0.5634 |
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| 0.7552 | 10.0 | 50 | 0.7181 | 0.5634 |
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| 0.7515 | 11.0 | 55 | 0.6951 | 0.5634 |
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| 0.7127 | 12.0 | 60 | 0.7140 | 0.4366 |
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| 0.7112 | 13.0 | 65 | 0.6901 | 0.5634 |
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| 0.6976 | 14.0 | 70 | 0.7009 | 0.4366 |
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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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