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bc2gm_corpus

参考:

bc2gm_corpus

使用以下命令在 TFDS 中加载此数据集:

ds = tfds.load('huggingface:bc2gm_corpus/bc2gm_corpus')
  • 说明
Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop.
In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions.
A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721.
Here we present brief descriptions of all the methods used and a statistical analysis of the results.
We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible,
and furthermore that the best result makes use of the lowest scoring submissions.

For more details, see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/

The original dataset can be downloaded from: https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-ii-corpus/
This dataset has been converted to CoNLL format for NER using the following tool: https://github.com/spyysalo/standoff2conll
  • 许可:无已知许可
  • 版本:1.0.0
  • 拆分
拆分 样本
'test' 5001
'train' 12501
'validation' 2501
  • 特征
{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "tokens": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "ner_tags": {
        "feature": {
            "num_classes": 3,
            "names": [
                "O",
                "B-GENE",
                "I-GENE"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}