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--- |
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library_name: transformers |
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datasets: |
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- abisee/cnn_dailymail |
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language: |
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- en |
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base_model: |
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- google-t5/t5-small |
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pipeline_tag: summarization |
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--- |
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# Fine-tunined the t5-small model |
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This is a text summarization fine-tuned model based on t5-small architecture with cnn_dailymail dataset. |
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## Usage |
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``` |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("JayasakthiBalaji/Text_Summarization_2e-5") |
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model = AutoModelForSeq2SeqLM.from_pretrained("JayasakthiBalaji/Text_Summarization_2e-5") |
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text = "Type your long story for summarization...." |
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inputs = tokenizer("summarize: " + text, return_tensors="pt", max_length=512, truncation=True) |
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outputs = model.generate(inputs.input_ids, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True) |
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summary = tokenizer.decode(outputs, skip_special_tokens=True) |
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print(summary) |
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``` |
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