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--- |
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library_name: transformers |
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language: |
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- en |
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license: apache-2.0 |
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base_model: google/bert_uncased_L-2_H-128_A-2 |
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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: bert_uncased_L-2_H-128_A-2_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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args: mrpc |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7328431372549019 |
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- name: F1 |
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type: f1 |
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value: 0.8233387358184764 |
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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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# bert_uncased_L-2_H-128_A-2_mrpc |
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This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the GLUE MRPC dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5352 |
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- Accuracy: 0.7328 |
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- F1: 0.8233 |
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- Combined Score: 0.7781 |
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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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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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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 | F1 | Combined Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| |
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| 0.6493 | 1.0 | 15 | 0.6227 | 0.6838 | 0.8122 | 0.7480 | |
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| 0.6257 | 2.0 | 30 | 0.6134 | 0.6838 | 0.8122 | 0.7480 | |
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| 0.6126 | 3.0 | 45 | 0.6052 | 0.6838 | 0.8122 | 0.7480 | |
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| 0.6036 | 4.0 | 60 | 0.5954 | 0.6961 | 0.8176 | 0.7569 | |
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| 0.5897 | 5.0 | 75 | 0.5879 | 0.6985 | 0.8167 | 0.7576 | |
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| 0.5781 | 6.0 | 90 | 0.5741 | 0.7034 | 0.8158 | 0.7596 | |
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| 0.5635 | 7.0 | 105 | 0.5711 | 0.7108 | 0.8201 | 0.7655 | |
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| 0.5429 | 8.0 | 120 | 0.5674 | 0.7132 | 0.8208 | 0.7670 | |
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| 0.5228 | 9.0 | 135 | 0.5685 | 0.7206 | 0.8252 | 0.7729 | |
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| 0.5057 | 10.0 | 150 | 0.5497 | 0.7304 | 0.8281 | 0.7793 | |
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| 0.4856 | 11.0 | 165 | 0.5438 | 0.7377 | 0.8293 | 0.7835 | |
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| 0.4657 | 12.0 | 180 | 0.5352 | 0.7328 | 0.8233 | 0.7781 | |
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| 0.4447 | 13.0 | 195 | 0.5435 | 0.7402 | 0.8323 | 0.7862 | |
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| 0.4175 | 14.0 | 210 | 0.5562 | 0.7402 | 0.8328 | 0.7865 | |
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| 0.4039 | 15.0 | 225 | 0.5759 | 0.7426 | 0.8357 | 0.7892 | |
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| 0.3964 | 16.0 | 240 | 0.5610 | 0.7377 | 0.8299 | 0.7838 | |
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| 0.3735 | 17.0 | 255 | 0.5587 | 0.7377 | 0.8283 | 0.7830 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.2.1+cu118 |
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- Datasets 2.17.0 |
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- Tokenizers 0.20.3 |
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