Twitter Roberta Base Dec2021 Tweetner7 Continuous

🚀 tner/twitter-roberta-base-dec2021-tweetner7-continuous

本模型是在 tner/tweetner7 数据集（train_2021 分割）上对 tner/twitter-roberta-base-dec2021-tweetner-2020 进行微调后的版本。该模型先在 train_2020 上进行微调，然后在 train_2021 上继续微调。模型微调通过 T-NER 的超参数搜索完成（更多细节请参阅仓库）。

🚀 快速开始

本模型可通过 tner 库 使用。通过 pip 安装该库：

pip install tner

TweetNER7 对推文进行了预处理，将账户名和 URL 转换为特殊格式（更多细节请参阅数据集页面）。因此，我们需要对推文进行相应处理，然后运行模型预测，示例如下：

import re
from urlextract import URLExtract
from tner import TransformersNER

extractor = URLExtract()

def format_tweet(tweet):
    # mask web urls
    urls = extractor.find_urls(tweet)
    for url in urls:
        tweet = tweet.replace(url, "{{URL}}")
    # format twitter account
    tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
    return tweet


text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/twitter-roberta-base-dec2021-tweetner7-continuous")
model.predict([text_format])

也可以通过 transformers 库使用该模型，但目前不推荐，因为暂不支持 CRF 层。

✨ 主要特性

• 基于预训练模型微调，在特定数据集上持续优化。
• 支持对推文进行命名实体识别。
• 提供了详细的评估指标和训练超参数。

📦 安装指南

通过 pip 安装 tner 库：

pip install tner

💻 使用示例

基础用法

import re
from urlextract import URLExtract
from tner import TransformersNER

extractor = URLExtract()

def format_tweet(tweet):
    # mask web urls
    urls = extractor.find_urls(tweet)
    for url in urls:
        tweet = tweet.replace(url, "{{URL}}")
    # format twitter account
    tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
    return tweet


text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/twitter-roberta-base-dec2021-tweetner7-continuous")
model.predict([text_format])

📚 详细文档

评估指标

训练超参数

以下是训练过程中使用的超参数：

• 数据集: ['tner/tweetner7']
• 数据集分割: train_2021
• 数据集名称: None
• 本地数据集: None
• 模型: tner/twitter-roberta-base-dec2021-tweetner-2020
• CRF: True
• 最大长度: 128
• 训练轮数: 30
• 批次大小: 32
• 学习率: 1e-06
• 随机种子: 0
• 梯度累积步数: 1
• 权重衰减: 1e-07
• 学习率热身步数比例: 0.15
• 最大梯度范数: 1

完整配置可在 微调参数文件 中查看。

引用

如果使用该模型，请引用以下论文：

• T-NER

@inproceedings{ushio-camacho-collados-2021-ner,
    title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
    author = "Ushio, Asahi  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eacl-demos.7",
    doi = "10.18653/v1/2021.eacl-demos.7",
    pages = "53--62",
    abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}

• TweetNER7

@inproceedings{ushio-etal-2022-tweet,
    title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
    author = "Ushio, Asahi  and
        Neves, Leonardo  and
        Silva, Vitor  and
        Barbieri, Francesco. and
        Camacho-Collados, Jose",
    booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
    month = nov,
    year = "2022",
    address = "Online",
    publisher = "Association for Computational Linguistics",
}