BC2GM#

Overview#

The BC2GM (BioCreative II Gene Mention) dataset is a widely used corpus for gene mention recognition, consisting of 20,000 sentences from MEDLINE abstracts where gene and protein names have been manually annotated by domain experts.

Task Description#

Task Type: Biomedical Named Entity Recognition (NER)
Input: Biomedical text from MEDLINE abstracts
Output: Identified gene and protein name spans
Domain: Molecular biology, genetics

Key Features#

20,000 sentences from MEDLINE abstracts
Expert-annotated gene and protein mentions
Benchmark from BioCreative II challenge
Widely used for biomedical NER evaluation
High-quality manual annotations

Evaluation Notes#

Default configuration uses 5-shot evaluation
Metrics: Precision, Recall, F1-Score, Accuracy
Entity types: GENE (gene and protein names)

Properties#

Property	Value
Benchmark Name	`bc2gm`
Dataset ID	extraordinarylab/bc2gm
Paper	N/A
Tags	`Knowledge`, `NER`
Metrics	`precision`, `recall`, `f1_score`, `accuracy`
Default Shots	5-shot
Evaluation Split	`test`
Train Split	`train`

Data Statistics#

Metric	Value
Total Samples	5,000
Prompt Length (Mean)	2228.52 chars
Prompt Length (Min/Max)	2073 / 3129 chars

Sample Example#

Subset: default

{
  "input": [
    {
      "id": "06f75061",
      "content": "Here are some examples of named entity recognition:\n\nInput:\nComparison with alkaline phosphatases and 5 - nucleotidase\n\nOutput:\n<response>Comparison with <gene>alkaline phosphatases</gene> and 5 - nucleotidase</response>\n\nInput:\nPharmacologic ... [TRUNCATED] ...  most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\nPhenotypic analysis demonstrates that trio and Abl cooperate in regulating axon outgrowth in the embryonic central nervous system ( CNS ) .\n"
    }
  ],
  "target": "<response>Phenotypic analysis demonstrates that <gene>trio</gene> and <gene>Abl</gene> cooperate in regulating axon outgrowth in the embryonic central nervous system ( CNS ) .</response>",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "tokens": [
      "Phenotypic",
      "analysis",
      "demonstrates",
      "that",
      "trio",
      "and",
      "Abl",
      "cooperate",
      "in",
      "regulating",
      "axon",
      "outgrowth",
      "in",
      "the",
      "embryonic",
      "central",
      "nervous",
      "system",
      "(",
      "CNS",
      ")",
      "."
    ],
    "ner_tags": [
      "O",
      "O",
      "O",
      "O",
      "B-GENE",
      "O",
      "B-GENE",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O"
    ]
  }
}

Note: Some content was truncated for display.

Prompt Template#

Prompt Template:

You are a named entity recognition system that identifies the following entity types:
{entities}

Process the provided text and mark all named entities with XML-style tags.

For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.

Available entity tags: {entity_list}

INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.

Text to process:
{text}

Few-shot Template

Here are some examples of named entity recognition:

{fewshot}

You are a named entity recognition system that identifies the following entity types:
{entities}

Process the provided text and mark all named entities with XML-style tags.

For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.

Available entity tags: {entity_list}

INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.

Text to process:
{text}

Usage#

Using CLI#

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets bc2gm \
    --limit 10  # Remove this line for formal evaluation

Using Python#

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['bc2gm'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)