207 lines
5.5 KiB
Markdown
207 lines
5.5 KiB
Markdown
# HarveyNER
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## 概述
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HarveyNER 是一个在飓风哈维（Hurricane Harvey）期间收集的推文数据集，其中标注了细粒度的位置信息。该数据集在非正式的危机相关描述中包含复杂且较长的位置提及，带来了独特的挑战。
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## 任务描述
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- **任务类型**：危机领域位置命名实体识别（NER）
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- **输入**：与飓风哈维相关的推文
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- **输出**：细粒度的位置实体片段
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- **领域**：危机通信、灾害响应
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## 主要特点
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- 推文中包含细粒度的位置标注
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- 位置提及复杂且长度较长
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- 非正式的危机相关文本
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- 四种位置实体类型（AREA、POINT、RIVER、ROAD）
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- 适用于灾害响应相关的自然语言处理应用
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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：AREA、POINT、RIVER、ROAD
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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** | `harvey_ner` |
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| **数据集ID** | [extraordinarylab/harvey-ner](https://modelscope.cn/datasets/extraordinarylab/harvey-ner/summary) |
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| **论文** | N/A |
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| **标签** | `Knowledge`, `NER` |
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| **指标** | `precision`, `recall`, `f1_score`, `accuracy` |
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| **默认示例数量** | 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 | 1,303 |
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| 提示词长度（平均） | 3018.97 字符 |
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| 提示词长度（最小/最大） | 2909 / 3199 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": "629de70e",
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"content": "Here are some examples of named entity recognition:\n\nInput:\nJust received word that UHVictoria and Bayou Oaks residents are in need of blankets , pillows , and clothes ( men & amp ; women ) . ( PT1 )\n\nOutput:\n<response>Just received word that ... [TRUNCATED] ... lap, choose the most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\nBREAKING : One firefighter injured after a fire / apparent explosion at the Lone Star Legal Aid Services in Downtown Houston\n"
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}
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],
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"target": "<response>BREAKING : One firefighter injured after a fire / apparent explosion at the <point>Lone Star Legal Aid Services in Downtown Houston</point></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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"BREAKING",
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":",
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"One",
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"firefighter",
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"injured",
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"after",
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"a",
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"fire",
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"/",
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"apparent",
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"explosion",
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"at",
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"the",
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"Lone",
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"Star",
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"Legal",
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"Aid",
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"Services",
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"in",
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"Downtown",
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"Houston"
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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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"B-POINT",
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"I-POINT",
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"I-POINT",
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"I-POINT",
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"I-POINT",
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"I-POINT",
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"I-POINT",
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"I-POINT"
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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 harvey_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=['harvey_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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``` |