# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("m3hrdadfi/bert2bert-fa-wiki-summary")
model = AutoModelForSeq2SeqLM.from_pretrained("m3hrdadfi/bert2bert-fa-wiki-summary")Quick Links
A Bert2Bert model on the Wiki Summary dataset to summarize articles. The model achieved an 8.47 ROUGE-2 score.
For more detail, please follow the Wiki Summary repo.
Eval results
The following table summarizes the ROUGE scores obtained by the Bert2Bert model.
| % | Precision | Recall | FMeasure |
|---|---|---|---|
| ROUGE-1 | 28.14 | 30.86 | 27.34 |
| ROUGE-2 | 07.12 | 08.47* | 07.10 |
| ROUGE-L | 28.49 | 25.87 | 25.50 |
Questions?
Post a Github issue on the Wiki Summary 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-wiki-summary")