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tags: |
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- pytorch |
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- bart |
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- faiss |
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library_name: transformers |
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--- |
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Model Name: BART-based Summarization Model |
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Model Details |
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This model is based on BART (Bidirectional and Auto-Regressive Transformers), a transformer-based model designed for sequence-to-sequence tasks like summarization, translation, and more. The specific model used here is facebook/bart-large-cnn, which has been fine-tuned on summarization tasks. |
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Model Type: BART (Large) |
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Model Architecture: Encoder-Decoder (Seq2Seq) |
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Framework: Hugging Face Transformers Library |
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Pretrained Model: facebook/bart-large-cnn |
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Model Description |
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This BART-based summarization model can generate summaries of long-form articles, such as news articles or research papers. It uses retrieval-augmented generation (RAG) principles, combining a retrieval system to augment model inputs for improved summarization. |
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How the Model Works: |
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Input Tokenization: The model takes in a long-form article (up to 1024 tokens) and converts it into tokenized input using the BART tokenizer. |
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RAG Application: Using Retrieval-Augmented Generation (RAG), the model is enhanced by leveraging a retrieval mechanism that provides additional context from an external knowledge source (if needed), though for this task it focuses on summarization without external retrieval. |
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Generation: The model generates a coherent summary of the input text using beam search for better fluency, with a maximum output length of 150 tokens. |
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Output: The generated text is a concise summary of the input article. |
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Intended Use |
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This model is ideal for summarizing long texts like news articles, research papers, and other written content where a brief overview is needed. The model aims to provide an accurate, concise representation of the original text. |
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Applications: |
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News summarization |
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Research article summarization |
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General content summarization |
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Example Usage |
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python |
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Copy code |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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# Load the tokenizer and model |
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model_name = "facebook/bart-large-cnn" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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# Sample article content |
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article = """ |
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As the world faces increasing challenges related to climate change and environmental degradation, renewable energy sources are becoming more important than ever. ... |
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""" |
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# Tokenize the input article |
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inputs = tokenizer(article, return_tensors="pt", max_length=1024, truncation=True) |
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# Generate summary |
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summary_ids = model.generate( |
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inputs['input_ids'], |
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max_length=150, |
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min_length=50, |
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length_penalty=2.0, |
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num_beams=4, |
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early_stopping=True |
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) |
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# Decode the summary |
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summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) |
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print("Generated Summary:", summary) |
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Model Parameters |
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Max input length: 1024 tokens |
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Max output length: 150 tokens |
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Min output length: 50 tokens |
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Beam search: 4 beams |
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Length penalty: 2.0 |
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Early stopping: Enabled |
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Limitations |
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Contextual Limitations: Summarization may lose some nuance, especially if important details appear toward the end of the article. Additionally, like most models, it may struggle with highly technical or domain-specific language. |
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Token Limitation: The model can only process up to 1024 tokens, so longer documents will need to be truncated. |
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Biases: As the model is trained on large datasets, it may inherit biases present in the data. |
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Future Work |
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Future improvements could involve incorporating a more robust retrieval mechanism to assist in generating even more accurate summaries, especially for domain-specific or technical articles. |
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Citation |
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If you use this model, please cite the original work on BART: |
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bibtex |
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Copy code |
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@article{lewis2019bart, |
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title={BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, |
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author={Lewis, Mike and Liu, Yinhan and Goyal, Naman and Ghazvininejad, Marjan and Mohamed, Abdelrahman and Levy, Omer and Stoyanov, Veselin and Zettlemoyer, Luke}, |
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journal={arXiv preprint arXiv:1910.13461}, |
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year={2019} |
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} |
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License |
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This model is licensed under the MIT License. |
