| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - glue |
| | metrics: |
| | - accuracy |
| | - f1 |
| | model-index: |
| | - name: distilbert-mrpc |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: glue |
| | type: glue |
| | args: mrpc |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.8480392156862745 |
| | - name: F1 |
| | type: f1 |
| | value: 0.8934707903780068 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # distilbert-mrpc |
| |
|
| | This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.6783 |
| | - Accuracy: 0.8480 |
| | - F1: 0.8935 |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 5e-05 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 3.0 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
| | | 0.5916 | 0.22 | 100 | 0.5676 | 0.7157 | 0.8034 | |
| | | 0.5229 | 0.44 | 200 | 0.4534 | 0.7770 | 0.8212 | |
| | | 0.5055 | 0.65 | 300 | 0.4037 | 0.8137 | 0.8762 | |
| | | 0.4597 | 0.87 | 400 | 0.3706 | 0.8407 | 0.8893 | |
| | | 0.4 | 1.09 | 500 | 0.4590 | 0.8113 | 0.8566 | |
| | | 0.3498 | 1.31 | 600 | 0.4196 | 0.8554 | 0.8974 | |
| | | 0.2916 | 1.53 | 700 | 0.4606 | 0.8554 | 0.8933 | |
| | | 0.3309 | 1.74 | 800 | 0.5162 | 0.8578 | 0.9027 | |
| | | 0.3788 | 1.96 | 900 | 0.3911 | 0.8529 | 0.8980 | |
| | | 0.2059 | 2.18 | 1000 | 0.5842 | 0.8554 | 0.8995 | |
| | | 0.1595 | 2.4 | 1100 | 0.5701 | 0.8578 | 0.8975 | |
| | | 0.1205 | 2.61 | 1200 | 0.6905 | 0.8407 | 0.8889 | |
| | | 0.174 | 2.83 | 1300 | 0.6783 | 0.8480 | 0.8935 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.15.0 |
| | - Pytorch 1.10.1 |
| | - Datasets 1.17.0 |
| | - Tokenizers 0.10.3 |
| |
|
