Create README.md

README.md

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+---
+datasets:
+- Samsung/samsum
+base_model:
+- google-t5/t5-small
+pipeline_tag: summarization
+---
+
+# T5 Small for Conversation Summarization
+
+## Usage
+
+```py
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+model_checkpoint = "ahlad/t5-small-finetuned-samsum"
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+
+input_text = """
+Emma: Did you finish the book I lent you? 
+Liam: Yes, I couldn’t put it down! The twist at the end was insane. 
+Emma: I know, right? I didn’t see it coming at all. What did you think of the main character? 
+Liam: Honestly, I thought they were a bit frustrating at first, but they grew on me. 
+Emma: Same here. I loved how they developed by the end. Are you up for another book from the series? 
+Liam: Absolutely! Pass it my way.
+"""
+
+inputs = tokenizer(input_text, return_tensors="pt")
+
+outputs = model.generate(**inputs)
+summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print("Summary:", summary)
+```