Instructions to use Vexemous/bart-based-finetuned-xsum-rougel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vexemous/bart-based-finetuned-xsum-rougel 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="Vexemous/bart-based-finetuned-xsum-rougel")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Vexemous/bart-based-finetuned-xsum-rougel") model = AutoModelForSeq2SeqLM.from_pretrained("Vexemous/bart-based-finetuned-xsum-rougel") - Notebooks
- Google Colab
- Kaggle
bart-base-finetuned-xsum-rougel
This model is a optimized version of Vexemous/bart-base-finetuned-xsum on the xsum dataset through rouge-L as the reward model with PPOTrainer
Model Details
Model Description
More information needed
Model Sources
More information needed
Framework versions
- Transformers 4.40.1
- Pytorch 1.13.1+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1
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