transformers safetensors bart text2text-generation pytorch faiss arxiv:1910.13461 autotrain_compatible endpoints_compatible region:us
---
library_name: transformers
tags:
- pytorch
- bart
- faiss
---



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.

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.

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.

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.

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.

Output: The generated text is a concise summary of the input article.

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.

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
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@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.