CrossNER

Overview

CrossNER is a fully-labeled collection of named entity recognition (NER) data spanning over five diverse domains: AI, Literature, Music, Politics, and Science. It enables cross-domain NER evaluation and domain adaptation research.

Task Description

Task Type: Cross-Domain Named Entity Recognition (NER)
Input: Text from five specialized domains
Output: Domain-specific entity spans
Domain: AI, Literature, Music, Politics, Science

Key Features

Five diverse domain subsets
Domain-specific entity types per subset
Enables cross-domain transfer evaluation
Fully labeled with expert annotations
Useful for domain adaptation research

Evaluation Notes

Default configuration uses 5-shot evaluation
Metrics: Precision, Recall, F1-Score, Accuracy
Subsets: ai, literature, music, politics, science
Entity types vary by domain subset

Properties

Property	Value
Benchmark Name	`cross_ner`
Dataset ID	extraordinarylab/cross-ner
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	2,506
Prompt Length (Mean)	5687.97 chars
Prompt Length (Min/Max)	5407 / 6007 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
`ai`	431	5562.3	5407	5878
`literature`	416	5725.13	5566	6007
`music`	465	5737.0	5570	5962
`politics`	651	5701.8	5527	5935
`science`	543	5700.71	5548	5995

Sample Example

Subset: ai

{
  "input": [
    {
      "id": "3a78cbcf",
      "content": "Here are some examples of named entity recognition:\n\nInput:\nPopular approaches of opinion-based recommender system utilize various techniques including text mining , information retrieval , sentiment analysis ( see also Multimodal sentiment a ... [TRUNCATED] ...  the most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\nTypical generative model approaches include naive Bayes classifier s , Gaussian mixture model s , variational autoencoders and others .\n"
    }
  ],
  "target": "<response>Typical generative model approaches include <algorithm>naive Bayes classifier</algorithm> s , <algorithm>Gaussian mixture model</algorithm> s , <algorithm>variational autoencoders</algorithm> and others .</response>",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "tokens": [
      "Typical",
      "generative",
      "model",
      "approaches",
      "include",
      "naive",
      "Bayes",
      "classifier",
      "s",
      ",",
      "Gaussian",
      "mixture",
      "model",
      "s",
      ",",
      "variational",
      "autoencoders",
      "and",
      "others",
      "."
    ],
    "ner_tags": [
      "O",
      "O",
      "O",
      "O",
      "O",
      "B-ALGORITHM",
      "I-ALGORITHM",
      "I-ALGORITHM",
      "O",
      "O",
      "B-ALGORITHM",
      "I-ALGORITHM",
      "I-ALGORITHM",
      "O",
      "O",
      "B-ALGORITHM",
      "I-ALGORITHM",
      "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 cross_ner \
    --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=['cross_ner'],
    dataset_args={
        'cross_ner': {
            # subset_list: ['ai', 'literature', 'music']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

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