--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: continue_pretrain_t5_base_10tokens results: [] --- # continue_pretrain_t5_base_10tokens This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.0015 - Rouge: {'rouge1': 0.15180664921665177, 'rouge2': 0.14241763000023774, 'rougeL': 0.1513952140128575, 'rougeLsum': 0.1517189553463021} - Exact Match: {'exact_match': 0.0} ## 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: 14 - eval_batch_size: 14 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 28 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------------------:|:--------------------:| | 0.1039 | 1.0 | 1786 | 4.8370 | {'rouge1': 0.08468086761955296, 'rouge2': 0.07928600729695852, 'rougeL': 0.08453698334268148, 'rougeLsum': 0.08473098923719942} | {'exact_match': 0.0} | | 0.0503 | 2.0 | 3572 | 4.9960 | {'rouge1': 0.15080775818986453, 'rouge2': 0.1414201900527639, 'rougeL': 0.15034957154685738, 'rougeLsum': 0.1506605398259596} | {'exact_match': 0.0} | | 0.0521 | 3.0 | 5358 | 5.0015 | {'rouge1': 0.15180664921665177, 'rouge2': 0.14241763000023774, 'rougeL': 0.1513952140128575, 'rougeLsum': 0.1517189553463021} | {'exact_match': 0.0} | ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.2+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1