--- library_name: transformers language: - en license: apache-2.0 base_model: gokulsrinivasagan/bert_base_train_book_ent_15p_s_init tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: bert_base_train_book_ent_15p_s_init_stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: .nan --- # bert_base_train_book_ent_15p_s_init_stsb This model is a fine-tuned version of [gokulsrinivasagan/bert_base_train_book_ent_15p_s_init](https://huggingface.co/gokulsrinivasagan/bert_base_train_book_ent_15p_s_init) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.3759 - Pearson: nan - Spearmanr: nan - Combined Score: nan ## 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 | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 3.2768 | 1.0 | 23 | 2.3872 | nan | nan | nan | | 2.2537 | 2.0 | 46 | 2.5971 | nan | nan | nan | | 2.1653 | 3.0 | 69 | 2.4241 | nan | nan | nan | | 2.1567 | 4.0 | 92 | 2.4241 | nan | nan | nan | | 2.1568 | 5.0 | 115 | 2.3759 | nan | nan | nan | | 2.1631 | 6.0 | 138 | 2.3759 | nan | nan | nan | | 2.1703 | 7.0 | 161 | 2.4954 | nan | nan | nan | | 2.1686 | 8.0 | 184 | 2.4954 | nan | nan | nan | | 2.1553 | 9.0 | 207 | 2.4512 | nan | nan | nan | | 2.1512 | 10.0 | 230 | 2.5971 | nan | nan | nan | ### Framework versions - Transformers 4.51.2 - Pytorch 2.6.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1