MultiNERD#

Overview#

MultiNERD is a large-scale, multilingual, and multi-genre dataset for fine-grained Named Entity Recognition, automatically generated from Wikipedia and Wikinews. It covers 10 languages and 15 distinct entity categories.

Task Description#

Task Type: Fine-grained Multilingual Named Entity Recognition (NER)
Input: Wikipedia and Wikinews text
Output: Identified entity spans with 15 fine-grained types
Domain: General knowledge, news, encyclopedic content

Key Features#

Large-scale automatically generated corpus
10 languages supported
15 fine-grained entity categories
Sourced from Wikipedia and Wikinews
Comprehensive entity type coverage

Evaluation Notes#

Default configuration uses 5-shot evaluation
Metrics: Precision, Recall, F1-Score, Accuracy
Entity types: PER, ORG, LOC, ANIM, BIO, CEL, DIS, EVE, FOOD, INST, MEDIA, MYTH, PLANT, TIME, VEHI

Properties#

Property	Value
Benchmark Name	`multi_nerd`
Dataset ID	extraordinarylab/multi-nerd
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	167,993
Prompt Length (Mean)	4016.26 chars
Prompt Length (Min/Max)	3915 / 4501 chars

Sample Example#

Subset: default

{
  "input": [
    {
      "id": "56cf0758",
      "content": "Here are some examples of named entity recognition:\n\nInput:\n2002 ging er ins Ausland und wechselte für 750.000 Pfund Sterling zu Manchester City .\n\nOutput:\n<response>2002 ging er ins Ausland und wechselte für 750.000 Pfund Sterling zu <organi ... [TRUNCATED] ...  Ensure every opening tag has a matching closing tag.\n\nText to process:\nIn der Wissenschaft und dort vor allem in der Soziologie wird der Begriff Lebensführung traditionell stark mit der religionshistorischen Arbeit von Max Weber verbunden .\n"
    }
  ],
  "target": "<response>In der Wissenschaft und dort vor allem in der Soziologie wird der Begriff Lebensführung traditionell stark mit der religionshistorischen Arbeit von <person>Max Weber</person> verbunden .</response>",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "tokens": [
      "In",
      "der",
      "Wissenschaft",
      "und",
      "dort",
      "vor",
      "allem",
      "in",
      "der",
      "Soziologie",
      "wird",
      "der",
      "Begriff",
      "Lebensführung",
      "traditionell",
      "stark",
      "mit",
      "der",
      "religionshistorischen",
      "Arbeit",
      "von",
      "Max",
      "Weber",
      "verbunden",
      "."
    ],
    "ner_tags": [
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "B-PER",
      "I-PER",
      "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 multi_nerd \
    --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=['multi_nerd'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)