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