# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("m3hrdadfi/bert2bert-fa-news-headline")
model = AutoModelForSeq2SeqLM.from_pretrained("m3hrdadfi/bert2bert-fa-news-headline")Quick Links
A Bert2Bert model on VoA Persian Corpus (a medium-sized corpus of 7.9 million words, 2003-2008) generates headlines. The model achieved a 25.30 ROUGE-2 score.
For more detail, please follow the News Headline Generation repo.
Eval results
The following table summarizes the ROUGE scores obtained by the Bert2Bert model.
| % | Precision | Recall | FMeasure |
|---|---|---|---|
| ROUGE-1 | 43.78 | 45.52 | 43.54 |
| ROUGE-2 | 24.50 | 25.30* | 24.24 |
| ROUGE-L | 41.20 | 42.22 | 40.76 |
Questions?
Post a Github issue on the News Headline Generation repo.
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# 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="m3hrdadfi/bert2bert-fa-news-headline")