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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: 0.8774509803921569 |
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- name: F1 |
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type: f1 |
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value: 0.9137931034482758 |
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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: train |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.979825517993457 |
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verified: true |
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- name: Precision |
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type: precision |
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value: 0.9842615012106537 |
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verified: true |
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- name: Recall |
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type: recall |
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value: 0.9858528698464026 |
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verified: true |
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- name: AUC |
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type: auc |
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value: 0.9958293217637636 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 0.9850565428109854 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.08004990220069885 |
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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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# roberta-base-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: 0.5565 |
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- Accuracy: 0.8775 |
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- F1: 0.9138 |
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- Combined Score: 0.8956 |
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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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- num_epochs: 5.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.0+cu102 |
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- Datasets 2.1.0 |
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- Tokenizers 0.11.6 |
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