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--- |
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language: |
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- en |
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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 MRPC |
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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: 0.6936274509803921 |
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- name: F1 |
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type: f1 |
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value: 0.8085758039816232 |
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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 MRPC dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5772 |
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- Accuracy: 0.6936 |
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- F1: 0.8086 |
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- Combined Score: 0.7511 |
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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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