--- license: apache-2.0 base_model: bert-large-uncased tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: bert-large-uncased_finetuning results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: validation args: en-US metrics: - name: Accuracy type: accuracy value: 0.863483523873571 --- # bert-large-uncased_finetuning This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.6719 - Accuracy: 0.8635 ## 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: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.748 | 1.39 | 500 | 0.6449 | 0.8426 | | 0.5674 | 2.78 | 1000 | 0.6501 | 0.8564 | | 0.385 | 4.17 | 1500 | 0.6410 | 0.8623 | | 0.2833 | 5.56 | 2000 | 0.6784 | 0.8495 | | 0.202 | 6.94 | 2500 | 0.7068 | 0.8716 | | 0.1405 | 8.33 | 3000 | 0.7838 | 0.8770 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0