🚀 NorBERT 3 xs
NorBERT 3 xs是新一代NorBERT语言模型的官方版本,该模型在论文NorBench — A Benchmark for Norwegian Language Models中有详细描述。若想了解该模型的更多细节,请阅读此论文。
🚀 快速开始
NorBERT 3 xs是基于挪威语的语言模型,在多种自然语言处理任务中表现出色。它提供了不同大小的版本,以满足不同场景的需求。
✨ 主要特性
- 多版本选择:提供了不同大小的版本,包括xs、small、base和large,可根据实际需求选择合适的模型。
- 多种任务支持:实现了多种类,如
AutoModel
、AutoModelMaskedLM
、AutoModelForSequenceClassification
等,支持多种自然语言处理任务。
📦 相关模型链接
其他尺寸的NorBERT 3模型
生成式NorT5系列模型
💻 使用示例
基础用法
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-xs")
model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-xs", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
print(tokenizer.decode(output_text[0].tolist()))
此模型目前需要来自modeling_norbert.py
的自定义包装器,因此你应该使用trust_remote_code=True
来加载模型。
支持的类
目前实现了以下类:AutoModel
、AutoModelMaskedLM
、AutoModelForSequenceClassification
、AutoModelForTokenClassification
、AutoModelForQuestionAnswering
和AutoModeltForMultipleChoice
。
📚 引用信息
@inproceedings{samuel-etal-2023-norbench,
title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models",
author = "Samuel, David and
Kutuzov, Andrey and
Touileb, Samia and
Velldal, Erik and
{\O}vrelid, Lilja and
R{\o}nningstad, Egil and
Sigdel, Elina and
Palatkina, Anna",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.61",
pages = "618--633",
abstract = "We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.",
}
📄 许可证
本项目采用apache-2.0
许可证。