--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: electra-base-discriminator-finetuned-rte results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.7942238267148014 --- # electra-base-discriminator-finetuned-rte This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4925 - Accuracy: 0.7942 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 156 | 0.6364 | 0.6390 | | No log | 2.0 | 312 | 0.5083 | 0.7653 | | No log | 3.0 | 468 | 0.4925 | 0.7942 | | 0.4688 | 4.0 | 624 | 0.7455 | 0.7690 | | 0.4688 | 5.0 | 780 | 0.7634 | 0.7762 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.10.3