Roberta Large Tweetner7 All

🚀 tner/roberta-large-tweetner7-all

tner/roberta-large-tweetner7-all 是一個基於 roberta-large 模型在 tner/tweetner7 數據集上微調得到的模型，用於命名實體識別任務。它能有效處理特定格式的推文數據，在相關評估指標上有不錯的表現。

✨ 主要特性

• 基於 roberta-large 模型進行微調，在 tner/tweetner7 數據集上有良好表現。
• 支持使用 tner 庫調用，方便快捷。
• 提供了詳細的評估指標和訓練超參數。

📦 安裝指南

此模型可通過 tner 庫 使用，可通過以下命令安裝該庫：

pip install tner

💻 使用示例

基礎用法

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

高級用法

暫未提供高級用法示例。

📚 詳細文檔

數據集和指標

模型結果

該模型在 2021 年測試集上的表現如下：

• F1 (micro): 0.6574551220340903
• Precision (micro): 0.644212629008989
• Recall (micro): 0.6712534690101758
• F1 (macro): 0.6124665667529737
• Precision (macro): 0.6005167968535563
• Recall (macro): 0.625251837701222

各實體的 F1 分數如下：

• corporation: 0.5392156862745098
• creative_work: 0.4760582928521859
• event: 0.4673321234119782
• group: 0.6139798488664987
• location: 0.6707399864222675
• person: 0.8293212669683258
• product: 0.6906187624750498

F1 分數的置信區間（通過自助法獲得）如下：

• F1 (micro):
  • 90%: [0.6484148010152769, 0.6672289519134409]
  • 95%: [0.6470100684797441, 0.6689850350992637]
• F1 (macro):
  • 90%: [0.6484148010152769, 0.6672289519134409]
  • 95%: [0.6470100684797441, 0.6689850350992637]

完整評估結果可參考 NER 評估文件 和 實體跨度評估文件。

訓練超參數

以下是訓練過程中使用的超參數：

• 數據集: ['tner/tweetner7']
• 數據集分割: train_all
• 數據集名稱: None
• 本地數據集: None
• 模型: roberta-large
• CRF: True
• 最大長度: 128
• 訓練輪數: 30
• 批次大小: 32
• 學習率: 1e-05
• 隨機種子: 0
• 梯度累積步數: 1
• 權重衰減: 1e-07
• 學習率熱身步數比例: 0.15
• 最大梯度範數: 1

完整配置可參考 微調參數文件。

🔧 技術細節

該模型基於 roberta-large 進行微調，使用了 CRF 層來提升性能。在訓練過程中，使用了特定的超參數進行優化，以在 tner/tweetner7 數據集上達到較好的效果。

📄 許可證

文檔未提及相關許可證信息。

📖 引用

如果使用該模型，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",
}