--- library_name: transformers language: - en base_model: Hartunka/tiny_bert_rand_10_v1 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: tiny_bert_rand_10_v1_rte results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.516245487364621 --- # tiny_bert_rand_10_v1_rte This model is a fine-tuned version of [Hartunka/tiny_bert_rand_10_v1](https://huggingface.co/Hartunka/tiny_bert_rand_10_v1) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6918 - Accuracy: 0.5162 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7007 | 1.0 | 10 | 0.6918 | 0.5162 | | 0.6835 | 2.0 | 20 | 0.6920 | 0.5596 | | 0.6616 | 3.0 | 30 | 0.7088 | 0.5740 | | 0.6219 | 4.0 | 40 | 0.7803 | 0.5523 | | 0.5651 | 5.0 | 50 | 0.8309 | 0.5379 | | 0.4986 | 6.0 | 60 | 0.9334 | 0.5126 | ### Framework versions - Transformers 4.50.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.21.1