| --- |
| library_name: transformers |
| language: |
| - en |
| license: apache-2.0 |
| base_model: google/bert_uncased_L-2_H-128_A-2 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - glue |
| metrics: |
| - accuracy |
| model-index: |
| - name: bert_uncased_L-2_H-128_A-2_mnli |
| results: |
| - task: |
| name: Text Classification |
| type: text-classification |
| dataset: |
| name: GLUE MNLI |
| type: glue |
| args: mnli |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.7112489829129374 |
| --- |
| |
| <!-- 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. --> |
|
|
| # bert_uncased_L-2_H-128_A-2_mnli |
| |
| 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 MNLI dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6901 |
| - Accuracy: 0.7112 |
| |
| ## 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: 256 |
| - eval_batch_size: 256 |
| - seed: 10 |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 50 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:| |
| | 0.9155 | 1.0 | 1534 | 0.8197 | 0.6342 | |
| | 0.8189 | 2.0 | 3068 | 0.7689 | 0.6626 | |
| | 0.7747 | 3.0 | 4602 | 0.7417 | 0.6760 | |
| | 0.7449 | 4.0 | 6136 | 0.7285 | 0.6852 | |
| | 0.7198 | 5.0 | 7670 | 0.7111 | 0.6934 | |
| | 0.6996 | 6.0 | 9204 | 0.7118 | 0.6977 | |
| | 0.6812 | 7.0 | 10738 | 0.7005 | 0.7030 | |
| | 0.6649 | 8.0 | 12272 | 0.6981 | 0.7043 | |
| | 0.6491 | 9.0 | 13806 | 0.7057 | 0.7036 | |
| | 0.6358 | 10.0 | 15340 | 0.6983 | 0.7077 | |
| | 0.6224 | 11.0 | 16874 | 0.6966 | 0.7064 | |
| | 0.6109 | 12.0 | 18408 | 0.7001 | 0.7145 | |
| | 0.5994 | 13.0 | 19942 | 0.7014 | 0.7113 | |
| | 0.5872 | 14.0 | 21476 | 0.7061 | 0.7084 | |
| | 0.5779 | 15.0 | 23010 | 0.7054 | 0.7168 | |
| | 0.5681 | 16.0 | 24544 | 0.7059 | 0.7147 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.46.3 |
| - Pytorch 2.2.1+cu118 |
| - Datasets 2.17.0 |
| - Tokenizers 0.20.3 |
| |
