TweeBankNER#

Overview#

Tweebank-NER is an English Twitter corpus created by annotating the syntactically-parsed Tweebank V2 with four types of named entities: Person, Organization, Location, and Miscellaneous. It addresses NER challenges in informal social media text.

Task Description#

Task Type: Social Media Named Entity Recognition (NER)
Input: Twitter text (tweets)
Output: Identified entity spans with types
Domain: Social media, informal text

Key Features#

Based on Tweebank V2 syntactic annotations
Four standard NER entity types
Addresses informal language challenges
Handles Twitter-specific text features (hashtags, mentions)
Useful for social media NLP applications

Evaluation Notes#

Default configuration uses 5-shot evaluation
Metrics: Precision, Recall, F1-Score, Accuracy
Entity types: PER, ORG, LOC, MISC

Properties#

Property	Value
Benchmark Name	`tweebank_ner`
Dataset ID	extraordinarylab/tweebank-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	1,201
Prompt Length (Mean)	2322.67 chars
Prompt Length (Min/Max)	2250 / 2398 chars

Sample Example#

Subset: default

{
  "input": [
    {
      "id": "46ad4b5f",
      "content": "Here are some examples of named entity recognition:\n\nInput:\nRT @USER2362 : Farmall Heart Of The Holidays Tabletop Christmas Tree With Lights And Motion URL1087 #Holiday #Gifts\n\nOutput:\n<response>RT @USER2362 : <organization>Farmall</organizat ... [TRUNCATED] ... include explanations, just the tagged text.\n6. If entity spans overlap, choose the most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\n@USER1812 No , I 'm not . It 's definitely not a rapper .\n"
    }
  ],
  "target": "<response>@USER1812 No , I 'm not . It 's definitely not a rapper .</response>",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "tokens": [
      "@USER1812",
      "No",
      ",",
      "I",
      "'m",
      "not",
      ".",
      "It",
      "'s",
      "definitely",
      "not",
      "a",
      "rapper",
      "."
    ],
    "ner_tags": [
      "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 tweebank_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=['tweebank_ner'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)