Create README.md

README.md

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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
+{}
+---
+
+# 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"
+
+
+### Training Data
+