tner/roberta-large-fin

该模型是在 tner/fin 数据集上 roberta-large 的微调版本。通过 T-NER 的超参数搜索进行模型微调（请参阅存储库了解更多详细信息）。它在测试集上实现了以下结果：

• F1（微观）：0.6988727858293075
• 精确度（微观）：0.7161716171617162
• 召回率（微观）：0.6823899371069182
• F1（宏观）：0.45636958249281745
• 精确度（宏观）：0.4519134760270864
• 召回率（宏观）：0.4705942205942206

测试集上F1分数的实体分级如下：

• 位置：0.5121951219512196
• 组织机构：0.49624060150375937
• 其他：0.0
• 个人：0.8170426065162907

对于F1得分，使用自助法获得置信区间，如下所示：

• F1（微观）：
  • 90％：[0.6355508274231678, 0.7613829748047737]
  • 95％：[0.624150263185174, 0.7724430709173716]
• F1（宏观）：
  • 90％：[0.6355508274231678, 0.7613829748047737]
  • 95％：[0.624150263185174, 0.7724430709173716]

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

使用

可以通过 tner library 来使用此模型。通过pip安装库

pip install tner

并按如下方式激活模型。

from tner import TransformersNER
model = TransformersNER("tner/roberta-large-fin")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])

可以通过transformers库使用它，但是目前不支持CRF层，因此不建议使用。

训练超参数

训练过程中使用了以下超参数：

• 数据集：['tner/fin']
• 数据集拆分：train
• 数据集名称：None
• 本地数据集：None
• 模型：roberta-large
• crf：True
• 最大长度：128
• epoch：15
• 批量大小：64
• lr：1e-05
• 随机种子：42
• 梯度累积步数：1
• 权重衰减：None
• lr_warmup_step_ratio：0.1
• 最大梯度规范：10.0

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

参考

如果你使用了T-NER的任何资源，请考虑引用我们的 paper 。

@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.",
}