legacy-datasets/wikipedia
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How to use lanwuwei/GigaBERT-v3-Arabic-and-English with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="lanwuwei/GigaBERT-v3-Arabic-and-English") # Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("lanwuwei/GigaBERT-v3-Arabic-and-English")
model = AutoModel.from_pretrained("lanwuwei/GigaBERT-v3-Arabic-and-English", device_map="auto")GigaBERT-v3 is a customized bilingual BERT for English and Arabic. It was pre-trained in a large-scale corpus (Gigaword+Oscar+Wikipedia) with ~10B tokens, showing state-of-the-art zero-shot transfer performance from English to Arabic on information extraction (IE) tasks. More details can be found in the following paper:
@inproceedings{lan2020gigabert,
author = {Lan, Wuwei and Chen, Yang and Xu, Wei and Ritter, Alan},
title = {An Empirical Study of Pre-trained Transformers for Arabic Information Extraction},
booktitle = {Proceedings of The 2020 Conference on Empirical Methods on Natural Language Processing (EMNLP)},
year = {2020}
}
from transformers import *
tokenizer = BertTokenizer.from_pretrained("lanwuwei/GigaBERT-v3-Arabic-and-English", do_lower_case=True)
model = BertForTokenClassification.from_pretrained("lanwuwei/GigaBERT-v3-Arabic-and-English")
More code examples can be found here.