Instructions to use nghuyong/ernie-2.0-large-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nghuyong/ernie-2.0-large-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nghuyong/ernie-2.0-large-en")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-2.0-large-en") model = AutoModel.from_pretrained("nghuyong/ernie-2.0-large-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
ERNIE-2.0-large
Introduction
ERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019, which builds and learns incrementally pre-training tasks through constant multi-task learning. Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLUE benchmarks and several common tasks in Chinese.
More detail: https://arxiv.org/abs/1907.12412
Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and a series of experiments have been conducted to check the accuracy of the conversion.
- Official PaddlePaddle ERNIE repo: https://github.com/PaddlePaddle/ERNIE
- Pytorch Conversion repo: https://github.com/nghuyong/ERNIE-Pytorch
How to use
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-2.0-large-en")
model = AutoModel.from_pretrained("nghuyong/ernie-2.0-large-en")
Citation
@article{sun2019ernie20,
title={ERNIE 2.0: A Continual Pre-training Framework for Language Understanding},
author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},
journal={arXiv preprint arXiv:1907.12412},
year={2019}
}
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