Training and benchmarking text-summarization models against Rouge score on the Samsum Dataset.
Rouge1 : 30.6 % Improvement
Rouge2 : 103 % Improvement
RougeL : 33.18 % Improvement
RougeLSum : 33.18 % Improvement
The fine-tuned model can be found at https://huggingface.co/dhivyeshrk/bart-large-cnn-samsum Runtime Logs and GPU utilization can be found in wandb_logs.pdf Trained on Nvidia Tesla P100
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
Open Streamlit_App_Text_Summarizer.ipynb for a live demo It is recommended to use colab.