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README.md

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+# Model discription
+Hindi Summarization model. It summarizes a hindi paragraph.
+
+# Base model 
+- mt5-small
+
+# How to use
+
+    from transformers import AutoTokenizer
+    from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
+
+    checkpoint = "Jayveersinh-Raj/hindi-summarizer-small"
+    tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+    model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
+
+    # Input paragraph for summarization
+    input_sentence = "<sum> your hindi paragraph"
+
+    # Tokenize the input sentence
+    input_ids = tokenizer.encode(input_sentence, return_tensors="pt").to("cuda")
+
+    # Generate predictions
+    with torch.no_grad():
+       output_ids = model.generate(input_ids, max_new_tokens=200)
+
+    # Decode the generated output
+    output_sentence = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    # Print the generated output
+    print("Input:", input_sentence)
+    print("Summarized:", output_sentence)
+
+# Evaluation
+- Rogue1: 0.38
+- BLUE: 0.35