Mbart Large Cc25 Cnn Dailymail Nl Finetune

🚀 mbart-large-cc25-cnn-dailymail-nl

This is a fine - tuned version of mbart, designed for summarizing Dutch news articles.

🚀 Quick Start

How to Use

import transformers
undisputed_best_model = transformers.MBartForConditionalGeneration.from_pretrained(
    "ml6team/mbart-large-cc25-cnn-dailymail-nl-finetune"
)
tokenizer = transformers.MBartTokenizer.from_pretrained("facebook/mbart-large-cc25")
summarization_pipeline = transformers.pipeline(
    task="summarization",
    model=undisputed_best_model,
    tokenizer=tokenizer,
)
summarization_pipeline.model.config.decoder_start_token_id = tokenizer.lang_code_to_id[
    "nl_XX"
]
article = "Kan je dit even samenvatten alsjeblief."  # Dutch
summarization_pipeline(
    article,
    do_sample=True,
    top_p=0.75,
    top_k=50,
    # num_beams=4,
    min_length=50,
    early_stopping=True,
    truncation=True,
)[0]["summary_text"]

✨ Features

This model is a fine - tuned version of mbart. It is specifically intended for summarizing Dutch news articles. We also wrote a blog post about this model here.

📦 Installation

The installation process is mainly about using the transformers library. You can install it via pip install transformers if not already installed.

📚 Documentation

Intended Uses & Limitations

It's meant for summarizing Dutch news articles.

Training Data

Finetuned mbart with this dataset and another smaller dataset that we can't open source because we scraped it from the internet. For more information check out our blog post here.

📄 License

No license information is provided in the original document, so this section is skipped.

🔧 Technical Details

No specific technical details (more than 50 - word description) are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

import transformers
undisputed_best_model = transformers.MBartForConditionalGeneration.from_pretrained(
    "ml6team/mbart-large-cc25-cnn-dailymail-nl-finetune"
)
tokenizer = transformers.MBartTokenizer.from_pretrained("facebook/mbart-large-cc25")
summarization_pipeline = transformers.pipeline(
    task="summarization",
    model=undisputed_best_model,
    tokenizer=tokenizer,
)
summarization_pipeline.model.config.decoder_start_token_id = tokenizer.lang_code_to_id[
    "nl_XX"
]
article = "Kan je dit even samenvatten alsjeblief."  # Dutch
summarization_pipeline(
    article,
    do_sample=True,
    top_p=0.75,
    top_k=50,
    # num_beams=4,
    min_length=50,
    early_stopping=True,
    truncation=True,
)[0]["summary_text"]

Advanced Usage

There is no advanced usage example in the original document, so this part is not added.

📋 Information Table