--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model_index: - name: finetuned-bert-mrpc results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: mrpc metric: name: F1 type: f1 value: 0.8791946308724832 base_model: bert-base-cased --- # finetuned-bert-mrpc This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4917 - Accuracy: 0.8235 - F1: 0.8792 ## 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5382 | 1.0 | 230 | 0.4008 | 0.8456 | 0.8893 | | 0.3208 | 2.0 | 460 | 0.4182 | 0.8309 | 0.8844 | | 0.1587 | 3.0 | 690 | 0.4917 | 0.8235 | 0.8792 | ### Framework versions - Transformers 4.9.0.dev0 - Pytorch 1.8.1+cu111 - Datasets 1.8.1.dev0 - Tokenizers 0.10.1