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--- |
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license: apache-2.0 |
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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: distilbert-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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args: mrpc |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8480392156862745 |
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- name: F1 |
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type: f1 |
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value: 0.8934707903780068 |
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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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# distilbert-mrpc |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6783 |
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- Accuracy: 0.8480 |
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- F1: 0.8935 |
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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: 8 |
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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: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.5916 | 0.22 | 100 | 0.5676 | 0.7157 | 0.8034 | |
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| 0.5229 | 0.44 | 200 | 0.4534 | 0.7770 | 0.8212 | |
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| 0.5055 | 0.65 | 300 | 0.4037 | 0.8137 | 0.8762 | |
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| 0.4597 | 0.87 | 400 | 0.3706 | 0.8407 | 0.8893 | |
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| 0.4 | 1.09 | 500 | 0.4590 | 0.8113 | 0.8566 | |
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| 0.3498 | 1.31 | 600 | 0.4196 | 0.8554 | 0.8974 | |
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| 0.2916 | 1.53 | 700 | 0.4606 | 0.8554 | 0.8933 | |
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| 0.3309 | 1.74 | 800 | 0.5162 | 0.8578 | 0.9027 | |
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| 0.3788 | 1.96 | 900 | 0.3911 | 0.8529 | 0.8980 | |
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| 0.2059 | 2.18 | 1000 | 0.5842 | 0.8554 | 0.8995 | |
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| 0.1595 | 2.4 | 1100 | 0.5701 | 0.8578 | 0.8975 | |
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| 0.1205 | 2.61 | 1200 | 0.6905 | 0.8407 | 0.8889 | |
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| 0.174 | 2.83 | 1300 | 0.6783 | 0.8480 | 0.8935 | |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.1 |
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- Datasets 1.17.0 |
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- Tokenizers 0.10.3 |
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