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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Description |
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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. |
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This model is a developed and fine-tuned for enhanced performance on dialogue summarization as a part of NLP assignment. |
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## Model Details |
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# Loading summarization pipeline and model |
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summarizer = pipeline('summarization', model = '/content/BART_FINETUNED_TEXT_SUMMARY') |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [Anupriya Sen and Ashutosh Kumar for NLP learning purpose and based on BART architecture] |
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## How to Use |
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# Loading summarization pipeline and model |
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summarizer = pipeline('summarization', model = '/content/BART_FINETUNED_TEXT_SUMMARY') |
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give input |
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Model will provide the contextual output summary of a given paragraph or dialogue |
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conversation = '''Soma: Do you think it's a good idea to invest in stocks? |
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Emily: I'm skeptical. The market is very volatile, and you could lose money. |
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Sarah: True. But there's also a high upside, right? |
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''' |
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model(conversation) |
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## Training Details |
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evaluation_strategy = "epoch", |
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save_strategy = 'epoch', |
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load_best_model_at_end = True, |
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metric_for_best_model = 'eval_loss', |
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seed = 42, |
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learning_rate=2e-5, |
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per_device_train_batch_size=4, |
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per_device_eval_batch_size=4, |
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gradient_accumulation_steps=2, |
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weight_decay=0.01, |
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save_total_limit=2, |
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num_train_epochs=4, |
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predict_with_generate=True, |
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report_to="none" |
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