bart-large-cnn
Maintainer: facebook
959
🏷️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The bart-large-cnn model is a large-sized BART model that has been fine-tuned on the CNN Daily Mail dataset. BART is a transformer encoder-decoder model that was introduced in the paper "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension" by Lewis et al. The model was initially released in the fairseq repository. This particular checkpoint has been fine-tuned for text summarization tasks.
The mbart-large-50 model is a multilingual sequence-to-sequence model that was introduced in the paper "Multilingual Translation with Extensible Multilingual Pretraining and Finetuning". It is a multilingual extension of the original mBART model, covering a total of 50 languages. The model was pre-trained using a "Multilingual Denoising Pretraining" objective, where the model is tasked with reconstructing the original text from a noised version.
The roberta-large model is a large-sized RoBERTa model, which is a transformer model pre-trained on a large corpus of English data using a masked language modeling (MLM) objective. RoBERTa was introduced in the paper "RoBERTa: A Robustly Optimized BERT Pretraining Approach" and was first released in the fairseq repository.
The bert-large-uncased and bert-base-uncased models are large and base-sized BERT models, respectively, that were pre-trained on a large corpus of English data using a masked language modeling (MLM) objective and a next sentence prediction (NSP) objective. BERT was introduced in the paper "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" and first released in the Google-research/BERT repository.
The bert-base-multilingual-uncased model is a multilingual base-sized BERT model that was pre-trained on the 102 languages with the largest Wikipedias using the same MLM and NSP objectives as the English BERT models.
Model inputs and outputs
Inputs
- Text: The
bart-large-cnnmodel takes text as input, which can be used for tasks like text summarization.
Outputs
- Text: The
bart-large-cnnmodel generates text as output, which can be used for tasks like summarizing long-form text.
Capabilities
The bart-large-cnn model is particularly effective when fine-tuned for text generation tasks, such as summarization. It can take in a long-form text and generate a concise summary. The model's bidirectional encoder and autoregressive decoder allow it to capture both the context of the full text and generate fluent, coherent summaries.
What can I use it for?
You can use the bart-large-cnn model for text summarization tasks, such as summarizing news articles, academic papers, or other long-form text. By fine-tuning the model on your own dataset, you can create a customized summarization system tailored to your domain or use case.
Things to try
Try fine-tuning the bart-large-cnn model on your own text summarization dataset to see how it performs on your specific use case. You can also experiment with different hyperparameters, such as the learning rate or batch size, to optimize the model's performance. Additionally, you could try combining the bart-large-cnn model with other NLP techniques, such as extractive summarization or topic modeling, to create a more sophisticated summarization system.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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