| tags: | |
| - summarization | |
| - ur | |
| - encoder-decoder | |
| - xlm-roberta | |
| - Abstractive Summarization | |
| - roberta | |
| - generated_from_trainer | |
| datasets: | |
| - xlsum | |
| model-index: | |
| - name: xlmroberta2xlmroberta-finetune-summarization-ur | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # xlmroberta2xlmroberta-finetune-summarization-ur | |
| This model is a fine-tuned version of [](https://huggingface.co./) on the xlsum dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 5.4576 | |
| - Rouge-1: 26.51 | |
| - Rouge-2: 9.4 | |
| - Rouge-l: 23.21 | |
| - Gen Len: 19.99 | |
| - Bertscore: 68.15 | |
| ## 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: 6 | |
| - eval_batch_size: 6 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 48 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 250 | |
| - num_epochs: 5 | |
| - label_smoothing_factor: 0.1 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.19.4 | |
| - Pytorch 1.11.0+cu113 | |
| - Datasets 2.3.2 | |
| - Tokenizers 0.12.1 | |