BART_FINETUNED_TEXT_SUMMARY

Model Card for Model ID

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Description

Automatic Text Summarization is one of the most challenging and interesting problems in the field of Natural Language Processing (NLP). It is a process of generating a concise and meaningful summary of text from multiple text resources such as books, news articles, blog posts, research papers, emails, and tweets. This model is a developed and fine-tuned for enhanced performance on dialogue summarization as a part of NLP assignment.

Model Details

Loading summarization pipeline and model

summarizer = pipeline('summarization', model = '/content/BART_FINETUNED_TEXT_SUMMARY')

Model Description

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• Developed by: [Anupriya Sen and Ashutosh Kumar for NLP learning purpose and based on BART architecture]

How to Use

Loading summarization pipeline and model

summarizer = pipeline('summarization', model = '/content/BART_FINETUNED_TEXT_SUMMARY') give input Model will provide the contextual output summary of a given paragraph or dialogue

conversation = '''Soma: Do you think it's a good idea to invest in stocks? Emily: I'm skeptical. The market is very volatile, and you could lose money. Sarah: True. But there's also a high upside, right?
''' model(conversation)

Training Details

evaluation_strategy = "epoch", save_strategy = 'epoch', load_best_model_at_end = True, metric_for_best_model = 'eval_loss', seed = 42, learning_rate=2e-5, per_device_train_batch_size=4, per_device_eval_batch_size=4, gradient_accumulation_steps=2, weight_decay=0.01, save_total_limit=2, num_train_epochs=4, predict_with_generate=True, report_to="none"