| | --- |
| | tags: |
| | - pytorch |
| | - bart |
| | - faiss |
| | library_name: transformers |
| | --- |
| | |
| |
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| |
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| | Model Name: BART-based Summarization Model |
| | Model Details |
| | 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) |
| | Model Architecture: Encoder-Decoder (Seq2Seq) |
| | Framework: Hugging Face Transformers Library |
| | Pretrained Model: facebook/bart-large-cnn |
| | Model Description |
| | 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: |
| | 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 |
| | 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: |
| | News summarization |
| | Research article summarization |
| | General content summarization |
| | Example Usage |
| | python |
| | Copy code |
| | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| |
|
| | # Load the tokenizer and model |
| | model_name = "facebook/bart-large-cnn" |
| | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| | model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
| | |
| | # Sample article content |
| | article = """ |
| | As the world faces increasing challenges related to climate change and environmental degradation, renewable energy sources are becoming more important than ever. ... |
| | """ |
| | |
| | # Tokenize the input article |
| | inputs = tokenizer(article, return_tensors="pt", max_length=1024, truncation=True) |
| | |
| | # Generate summary |
| | summary_ids = model.generate( |
| | inputs['input_ids'], |
| | max_length=150, |
| | min_length=50, |
| | length_penalty=2.0, |
| | num_beams=4, |
| | early_stopping=True |
| | ) |
| | |
| | # Decode the summary |
| | summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) |
| | |
| | print("Generated Summary:", summary) |
| | Model Parameters |
| | Max input length: 1024 tokens |
| | Max output length: 150 tokens |
| | Min output length: 50 tokens |
| | Beam search: 4 beams |
| | Length penalty: 2.0 |
| | Early stopping: Enabled |
| | Limitations |
| | 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. |
| | Token Limitation: The model can only process up to 1024 tokens, so longer documents will need to be truncated. |
| | Biases: As the model is trained on large datasets, it may inherit biases present in the data. |
| | Future Work |
| | 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. |
| | |
| | Citation |
| | If you use this model, please cite the original work on BART: |
| | |
| | bibtex |
| | Copy code |
| | @article{lewis2019bart, |
| | title={BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, |
| | author={Lewis, Mike and Liu, Yinhan and Goyal, Naman and Ghazvininejad, Marjan and Mohamed, Abdelrahman and Levy, Omer and Stoyanov, Veselin and Zettlemoyer, Luke}, |
| | journal={arXiv preprint arXiv:1910.13461}, |
| | year={2019} |
| | } |
| | License |
| | This model is licensed under the MIT License. |
