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# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
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# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

## 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

<!-- Provide a longer summary of what this model is. -->



- **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"