bc2gm_corpus

References:

• Code
• Huggingface

bc2gm_corpus

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:bc2gm_corpus/bc2gm_corpus')

• Description:

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

• License: No known license
• Version: 1.0.0
• Splits:

• Features:

{
    "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"
    }
}