# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/roberta_shared_bbc_xsum")
model = AutoModelForSeq2SeqLM.from_pretrained("patrickvonplaten/roberta_shared_bbc_xsum")Quick Links
Shared RoBERTa2RoBERTa Summarization with 🤗EncoderDecoder Framework This model is a warm-started RoBERTaShared model fine-tuned on the BBC XSum summarization dataset.
The model achieves a 16.89 ROUGE-2 score on BBC XSUM's test dataset.
For more details on how the model was fine-tuned, please refer to this notebook.
- Downloads last month
- 8
# 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="patrickvonplaten/roberta_shared_bbc_xsum")