Roberta Base Tweetner7 All

🚀 tner/roberta-base-tweetner7-all

該模型是基於 roberta-base 在 tner/tweetner7 數據集（train_all 分割）上進行微調的版本。模型微調通過 T-NER 的超參數搜索完成（更多細節請參閱該倉庫）。

🚀 快速開始

本模型可通過 tner 庫 使用。首先，通過 pip 安裝該庫：

pip install tner

TweetNER7 對推文進行了預處理，將賬戶名和 URL 轉換為特殊格式（更多細節請參閱數據集頁面）。因此，我們需要對推文進行相應處理，然後再運行模型預測，示例代碼如下：

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])

該模型也可以通過 transformers 庫使用，但由於目前不支持 CRF 層，因此不建議這樣做。

✨ 主要特性

• 微調模型：基於 roberta-base 在 tner/tweetner7 數據集上微調，提升了在特定任務上的性能。
• 多指標評估：在測試集上提供了豐富的評估指標，包括 F1、Precision、Recall 等，且提供了每個實體類別的 F1 分數和置信區間。

📚 詳細文檔

評估結果

該模型在 2021 年測試集上取得了以下結果：

測試集上每個實體類別的 F1 分數細分如下：

對於 F1 分數，通過自助法獲得的置信區間如下：

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

完整的評估結果可在 NER 指標文件 和 實體跨度指標文件 中查看。

訓練超參數

訓練過程中使用的超參數如下：

完整的配置可在 微調參數文件 中查看。

引用

如果您使用了該模型，請引用 T-NER 論文和 TweetNER7 論文：

• 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",
}