ml6team/cnn_dailymail_nl
Updated • 105 • 14
How to use ml6team/mbart-large-cc25-cnn-dailymail-nl with Transformers:
# Use a pipeline as a high-level helper
# Warning: Pipeline type "summarization" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline
pipe = pipeline("summarization", model="ml6team/mbart-large-cc25-cnn-dailymail-nl") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("ml6team/mbart-large-cc25-cnn-dailymail-nl")
model = AutoModelForSeq2SeqLM.from_pretrained("ml6team/mbart-large-cc25-cnn-dailymail-nl", device_map="auto")Finetuned version of mbart. We also wrote a blog post about this model here
It's meant for summarizing Dutch news articles.
import transformers
undisputed_best_model = transformers.MBartForConditionalGeneration.from_pretrained(
"ml6team/mbart-large-cc25-cnn-dailymail-nl"
)
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"]
Finetuned mbart with this dataset