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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_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: 0.7622549019607843
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- name: F1
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type: f1
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value: 0.8380634390651085
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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_mrpc
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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: 0.9954
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- Accuracy: 0.7623
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- F1: 0.8381
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- Combined Score: 0.8002
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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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| 0.6388 | 1.0 | 15 | 0.6297 | 0.6838 | 0.8122 | 0.7480 |
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| 0.612 | 2.0 | 30 | 0.6315 | 0.6887 | 0.8135 | 0.7511 |
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| 0.5725 | 3.0 | 45 | 0.5772 | 0.6936 | 0.8086 | 0.7511 |
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| 0.512 | 4.0 | 60 | 0.6261 | 0.7010 | 0.8152 | 0.7581 |
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| 0.3924 | 5.0 | 75 | 0.6433 | 0.7279 | 0.8195 | 0.7737 |
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| 0.2592 | 6.0 | 90 | 0.7531 | 0.6863 | 0.7594 | 0.7228 |
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| 0.1689 | 7.0 | 105 | 0.7904 | 0.7377 | 0.8158 | 0.7768 |
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| 0.1292 | 8.0 | 120 | 0.9954 | 0.7623 | 0.8381 | 0.8002 |
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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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