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--- |
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license: mit |
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datasets: |
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- EdinburghNLP/xsum |
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pipeline_tag: summarization |
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--- |
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# BART Large CNN Text Summarization Model |
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This model is based on the Facebook BART (Bidirectional and Auto-Regressive Transformers) architecture, specifically the large variant fine-tuned for text summarization tasks. BART is a sequence-to-sequence model introduced by Facebook AI, capable of handling various natural language processing tasks, including summarization. |
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## Model Details: |
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- **Architecture**: BART Large CNN |
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- **Pre-trained model**: BART Large |
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- **Fine-tuned for**: Text Summarization |
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- **Fine-tuning dataset**: [xsum] |
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## Usage: |
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### Installation: |
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You can install the necessary libraries using pip: |
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```bash |
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pip install transformers |
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pip datasets |
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pip evaluate |
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pip rouge_score |
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### Inference |
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```bash |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("suriya7/text_summarize") |
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model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/text_summarize") |
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def generate_summary(text): |
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inputs = tokenizer([text], max_length=1024, return_tensors='pt', truncation=True) |
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summary_ids = model.generate(inputs['input_ids'],max_new_tokens=100, do_sample=False) |
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summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) |
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return summary |
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text_to_summarize = "Now, there is no doubt that one of the most important aspects of any Pixel phone is its camera. |
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And there might be good news for all camera lovers. Rumours have suggested that the Pixel 9 could come with a telephoto lens, |
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improving its photography capabilities even further. Google will likely continue to focus on using AI to |
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enhance its camera performance, in order to make sure that Pixel phones remain top contenders in the world of mobile photography" |
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summary = generate_summary(text_to_summarize) |
