--- library_name: transformers language: - en base_model: gokulsrinivasagan/bert_tiny_lda_5_v1_book tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: bert_tiny_lda_5_v1_book_mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.7181372549019608 - name: F1 type: f1 value: 0.8200312989045383 --- # bert_tiny_lda_5_v1_book_mrpc This model is a fine-tuned version of [gokulsrinivasagan/bert_tiny_lda_5_v1_book](https://huggingface.co/gokulsrinivasagan/bert_tiny_lda_5_v1_book) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5735 - Accuracy: 0.7181 - F1: 0.8200 - Combined Score: 0.7691 ## 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 | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6264 | 1.0 | 15 | 0.6012 | 0.6863 | 0.7994 | 0.7428 | | 0.5868 | 2.0 | 30 | 0.5820 | 0.6985 | 0.8006 | 0.7496 | | 0.5558 | 3.0 | 45 | 0.6051 | 0.6961 | 0.8144 | 0.7552 | | 0.5036 | 4.0 | 60 | 0.5735 | 0.7181 | 0.8200 | 0.7691 | | 0.4117 | 5.0 | 75 | 0.5969 | 0.7083 | 0.7980 | 0.7531 | | 0.32 | 6.0 | 90 | 0.6340 | 0.7328 | 0.8256 | 0.7792 | | 0.2656 | 7.0 | 105 | 0.9137 | 0.7181 | 0.8271 | 0.7726 | | 0.2023 | 8.0 | 120 | 0.8611 | 0.7230 | 0.8259 | 0.7745 | | 0.1604 | 9.0 | 135 | 0.9086 | 0.7328 | 0.8310 | 0.7819 | ### Framework versions - Transformers 4.46.3 - Pytorch 2.2.1+cu118 - Datasets 2.17.0 - Tokenizers 0.20.3