tner/roberta-large-tweetner7-all

该模型是在数据集（train_all分割）上对模型 roberta-large 进行微调的结果。模型的微调是通过进行 T-NER 的超参数搜索完成的（有关更多细节，请参见存储库）。它在2021年的测试集上实现了以下结果：

精确度（微平均）：0.644212629008989
召回率（微平均）：0.6712534690101758
精确度（宏平均）：0.6005167968535563
召回率（宏平均）：0.625251837701222

测试集中F1得分的每个实体的详细情况如下：

公司：0.5392156862745098
创造性作品：0.4760582928521859
事件：0.4673321234119782
团体：0.6139798488664987
地点：0.6707399864222675
人物：0.8293212669683258
产品：0.6906187624750498

F1分数的置信区间通过自举得到：

F1（微平均）：
- 90%：[0.6484148010152769，0.6672289519134409]
- 95%：[0.6470100684797441，0.6689850350992637]
F1（宏平均）：
- 90%：[0.6484148010152769，0.6672289519134409]
- 95%：[0.6470100684797441，0.6689850350992637]

完整的评估结果可以在 metric file of NER 和 metric file of entity span 找到。

使用方法

该模型可以通过 tner library 使用。通过pip安装该库。

pip install tner

对预处理的推文进行处理，其中账户名和URL被转换为特殊格式（有关更多详细信息，请参见数据集页面），因此我们根据此进行推文处理，然后进行模型预测，如下所示。

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-large-tweetner7-all")
model.predict([text_format])

可以通过transformers库使用它，但目前不推荐使用，因为不支持CRF层。

训练超参数

训练过程中使用了以下超参数：

数据集：['tner/tweetner7']
数据集分割：train_all
数据集名称：None
本地数据集：None
模型：roberta-large
CRF：True
最大长度：128
周期：30
批次大小：32
学习率：1e-05
随机种子：0
梯度累积步数：1
权重衰减：1e-07
学习率预热步数比率：0.15
最大梯度范数：1

完整的配置可以在 fine-tuning parameter file 找到。

参考文献

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",
}

作者:

TNER

数据集大小:

1.32 GB