Roberta Base Tweetner7 All

🚀 tner/roberta-base-tweetner7-all

该模型是基于 roberta-base 在 tner/tweetner7 数据集（train_all 分割）上进行微调的版本。模型微调通过 T-NER 的超参数搜索完成（更多细节请参阅该仓库）。

🚀 快速开始

本模型可通过 tner 库 使用。首先，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，然后再运行模型预测，示例代码如下：

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/roberta-base-tweetner7-all")
model.predict([text_format])

该模型也可以通过 transformers 库使用，但由于目前不支持 CRF 层，因此不建议这样做。

✨ 主要特性

• 微调模型：基于 roberta-base 在 tner/tweetner7 数据集上微调，提升了在特定任务上的性能。
• 多指标评估：在测试集上提供了丰富的评估指标，包括 F1、Precision、Recall 等，且提供了每个实体类别的 F1 分数和置信区间。

📚 详细文档

评估结果

该模型在 2021 年测试集上取得了以下结果：

测试集上每个实体类别的 F1 分数细分如下：

对于 F1 分数，通过自助法获得的置信区间如下：

• F1 (micro):
  • 90%: [0.6426248846161623, 0.6611146727643068]
  • 95%: [0.6408583849998567, 0.6629609445072536]
• F1 (macro):
  • 90%: [0.6426248846161623, 0.6611146727643068]
  • 95%: [0.6408583849998567, 0.6629609445072536]

完整的评估结果可在 NER 指标文件 和 实体跨度指标文件 中查看。

训练超参数

训练过程中使用的超参数如下：

完整的配置可在 微调参数文件 中查看。

引用

如果您使用了该模型，请引用 T-NER 论文和 TweetNER7 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",
}