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--- |
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language: |
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- en |
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license: mit |
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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: roberta-base-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: 0.9019607843137255 |
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- name: F1 |
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type: f1 |
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value: 0.9295774647887324 |
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- task: |
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type: natural-language-inference |
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name: Natural Language Inference |
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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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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9019607843137255 |
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verified: true |
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- name: Precision |
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type: precision |
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value: 0.9134948096885813 |
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verified: true |
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- name: Recall |
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type: recall |
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value: 0.946236559139785 |
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verified: true |
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- name: AUC |
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type: auc |
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value: 0.9536411880747964 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 0.9295774647887324 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.48942330479621887 |
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verified: true |
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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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# mrpc |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MRPC dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4898 |
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- Accuracy: 0.9020 |
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- F1: 0.9296 |
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- Combined Score: 0.9158 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- lr_scheduler_warmup_ratio: 0.06 |
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- num_epochs: 5.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.20.0.dev0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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