194 lines
5.2 KiB
Markdown
194 lines
5.2 KiB
Markdown
# TweeBankNER
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## 概述
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Tweebank-NER 是一个英文 Twitter 语料库，通过对已进行句法分析的 Tweebank V2 数据集标注四种命名实体类型（人物、组织、地点和杂项）构建而成。该数据集旨在应对社交媒体非正式文本中的命名实体识别（NER）挑战。
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## 任务描述
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- **任务类型**：社交媒体命名实体识别（NER）
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- **输入**：Twitter 文本（推文）
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- **输出**：识别出的实体片段及其类型
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- **领域**：社交媒体、非正式文本
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## 主要特点
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- 基于 Tweebank V2 的句法标注
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- 包含四种标准 NER 实体类型
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- 针对非正式语言带来的挑战
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- 处理 Twitter 特有的文本特征（如话题标签、用户提及）
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- 适用于社交媒体自然语言处理应用
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## 评估说明
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- 默认配置使用 **5-shot** 评估
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- 评估指标：精确率（Precision）、召回率（Recall）、F1 分数（F1-Score）、准确率（Accuracy）
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- 实体类型：PER（人物）、ORG（组织）、LOC（地点）、MISC（杂项）
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## 属性
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| 属性 | 值 |
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|----------|-------|
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| **基准测试名称** | `tweebank_ner` |
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| **数据集ID** | [extraordinarylab/tweebank-ner](https://modelscope.cn/datasets/extraordinarylab/tweebank-ner/summary) |
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| **论文** | 无 |
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| **标签** | `Knowledge`, `NER` |
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| **指标** | `precision`, `recall`, `f1_score`, `accuracy` |
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| **默认样本数** | 5-shot |
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| **评估划分** | `test` |
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| **训练划分** | `train` |
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## 数据统计
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| 指标 | 值 |
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|--------|-------|
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| 总样本数 | 1,201 |
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| 提示词长度（平均） | 2322.67 字符 |
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| 提示词长度（最小/最大） | 2250 / 2398 字符 |
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## 样例示例
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**子集**: `default`
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```json
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{
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"input": [
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{
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"id": "46ad4b5f",
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"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"
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}
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],
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"target": "<response>@USER1812 No , I 'm not . It 's definitely not a rapper .</response>",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"tokens": [
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"@USER1812",
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"No",
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",",
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"I",
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"'m",
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"not",
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".",
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"It",
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"'s",
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"definitely",
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"not",
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"a",
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"rapper",
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"."
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],
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"ner_tags": [
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O",
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"O"
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]
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}
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}
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```
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*注：部分内容为显示目的已被截断。*
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## 提示模板
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**提示模板：**
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```text
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You are a named entity recognition system that identifies the following entity types:
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{entities}
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Process the provided text and mark all named entities with XML-style tags.
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For example:
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<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
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Available entity tags: {entity_list}
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INSTRUCTIONS:
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1. Wrap your entire response in <response>...</response> tags.
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2. Inside these tags, include the original text with entity tags inserted.
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3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
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4. Tag ALL entities you can identify using the exact tag names provided.
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5. Do not include explanations, just the tagged text.
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6. If entity spans overlap, choose the most specific entity type.
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7. Ensure every opening tag has a matching closing tag.
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Text to process:
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{text}
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```
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<details>
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<summary>少样本（Few-shot）模板</summary>
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```text
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Here are some examples of named entity recognition:
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{fewshot}
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You are a named entity recognition system that identifies the following entity types:
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{entities}
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Process the provided text and mark all named entities with XML-style tags.
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For example:
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<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
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Available entity tags: {entity_list}
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INSTRUCTIONS:
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1. Wrap your entire response in <response>...</response> tags.
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2. Inside these tags, include the original text with entity tags inserted.
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3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
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4. Tag ALL entities you can identify using the exact tag names provided.
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5. Do not include explanations, just the tagged text.
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6. If entity spans overlap, choose the most specific entity type.
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7. Ensure every opening tag has a matching closing tag.
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Text to process:
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{text}
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```
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</details>
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## 使用方法
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### 使用 CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets tweebank_ner \
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--limit 10 # 正式评估时请删除此行
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```
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### 使用 Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['tweebank_ner'],
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limit=10, # 正式评估时请删除此行
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)
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run_task(task_cfg=task_cfg)
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``` |