Instructions to use praf-choub/bart-CaPE-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use praf-choub/bart-CaPE-xsum 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="praf-choub/bart-CaPE-xsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("praf-choub/bart-CaPE-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("praf-choub/bart-CaPE-xsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: en | |
| tags: | |
| - summarization | |
| license: bsd-3-clause | |
| datasets: | |
| - xsum | |
| Citation | |
| ``` | |
| @misc{https://doi.org/10.48550/arxiv.2110.07166, | |
| doi = {10.48550/ARXIV.2110.07166}, | |
| url = {https://arxiv.org/abs/2110.07166}, | |
| author = {Choubey, Prafulla Kumar and Fabbri, Alexander R. and Vig, Jesse and Wu, Chien-Sheng and Liu, Wenhao and Rajani, Nazneen Fatema}, | |
| keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, | |
| title = {CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization}, | |
| publisher = {arXiv}, | |
| year = {2021}, | |
| copyright = {Creative Commons Attribution 4.0 International} | |
| } | |
| ``` |