--- language: en tags: - sagemaker - bart - summarization license: apache-2.0 datasets: - samsum model-index: - name: bart-large-cnn-samsum results: - task: name: Abstractive Text Summarization type: abstractive-text-summarization dataset: name: "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization" type: samsum metrics: - name: Validation ROUGE-1 type: rouge-1 value: 43.2111 - name: Validation ROUGE-2 type: rouge-2 value: 22.3519 - name: Validation ROUGE-L type: rouge-l value: 33.315 - name: Test ROUGE-1 type: rouge-1 value: 41.8283 - name: Test ROUGE-2 type: rouge-2 value: 20.9857 - name: Test ROUGE-L type: rouge-l value: 32.3602 widget: - text: | Sugi: I am tired of everything in my life. Tommy: What? How happy you life is! I do envy you. Sugi: You don't know that I have been over-protected by my mother these years. I am really about to leave the family and spread my wings. Tommy: Maybe you are right. --- ## `bart-large-cnn-samsum` This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container. For more information look at: - [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html) - [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker) - [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html) - [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html) - [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers) ## Hyperparameters { "dataset_name": "samsum", "do_eval": true, "do_predict": true, "do_train": true, "fp16": true, "learning_rate": 5e-05, "model_name_or_path": "facebook/bart-large-cnn", "num_train_epochs": 3, "output_dir": "/opt/ml/model", "per_device_eval_batch_size": 4, "per_device_train_batch_size": 4, "predict_with_generate": true, "seed": 7 } ## Usage from transformers import pipeline summarizer = pipeline("summarization", model="slauw87/bart-large-cnn-samsum") conversation = '''Sugi: I am tired of everything in my life. Tommy: What? How happy you life is! I do envy you. Sugi: You don't know that I have been over-protected by my mother these years. I am really about to leave the family and spread my wings. Tommy: Maybe you are right. ''' nlp(conversation) ## Results | key | value | | --- | ----- | | eval_rouge1 | 43.2111 | | eval_rouge2 | 22.3519 | | eval_rougeL | 33.3153 | | eval_rougeLsum | 40.0527 | | predict_rouge1 | 41.8283 | | predict_rouge2 | 20.9857 | | predict_rougeL | 32.3602 | | predict_rougeLsum | 38.7316 |