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Model Card for Model ID
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
- 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"