234 lines
6.1 KiB
Markdown
234 lines
6.1 KiB
Markdown
# FinNER
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## 概述
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FinNER 数据集是一个来自美国证券交易委员会（SEC）公开文件中的金融协议语料库，标注了人物（Person）、组织（Organization）、地点（Location）和杂项（Miscellaneous）实体，用于支持信用风险评估中的信息抽取任务。
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## 任务描述
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- **任务类型**：金融命名实体识别（NER）
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- **输入**：来自 SEC 文件的金融协议文本
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- **输出**：识别出的实体片段及其类型
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- **领域**：金融、法律文档、信用风险
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## 主要特点
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- 来自 SEC 文件的金融协议
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- 针对信用风险评估应用进行标注
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- 采用适用于金融领域的标准 NER 实体类型
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- 专为金融文档处理设计
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- 适用于法律与金融领域的 AI 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** | `fin_ner` |
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| **数据集 ID** | [extraordinarylab/fin-ner](https://modelscope.cn/datasets/extraordinarylab/fin-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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| **评估集** | `test` |
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| **训练集** | `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 | 305 |
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| 提示词长度（平均） | 2891.13 字符 |
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| 提示词长度（最小/最大） | 2663 / 6149 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": "d8a8baf8",
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"content": "Here are some examples of named entity recognition:\n\nInput:\n( l ) \" Tranche A Shares \" has the meaning as defined in the Subscription Agreement .\n\nOutput:\n<response>( l ) \" Tranche A Shares \" has the meaning as defined in the Subscription Agr ... [TRUNCATED] ... osing tag.\n\nText to process:\nSubordinated Loan Agreement - Silicium de Provence SAS and Evergreen Solar Inc . 7 - December 2007 [ HERBERT SMITH LOGO ] ................................ 2007 SILICIUM DE PROVENCE SAS and EVERGREEN SOLAR , INC .\n"
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}
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],
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"target": "<response>Subordinated Loan Agreement - <organization>Silicium de Provence SAS</organization> and <organization>Evergreen Solar Inc</organization> . 7 - December 2007 [ <person>HERBERT SMITH</person> LOGO ] ................................ 2007 <organization>SILICIUM DE PROVENCE SAS</organization> and <organization>EVERGREEN SOLAR</organization> , INC .</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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"Subordinated",
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"Loan",
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"Agreement",
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"-",
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"Silicium",
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"de",
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"Provence",
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"SAS",
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"and",
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"Evergreen",
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"Solar",
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"Inc",
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".",
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"7",
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"-",
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"December",
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"2007",
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"[",
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"HERBERT",
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"SMITH",
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"LOGO",
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"]",
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"................................",
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"2007",
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"SILICIUM",
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"DE",
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"PROVENCE",
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"SAS",
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"and",
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"EVERGREEN",
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"SOLAR",
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",",
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"INC",
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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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"B-ORG",
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"I-ORG",
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"I-ORG",
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"I-ORG",
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"O",
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"B-ORG",
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"I-ORG",
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"I-ORG",
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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-PER",
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"I-PER",
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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-ORG",
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"I-ORG",
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"I-ORG",
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"I-ORG",
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"O",
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"B-ORG",
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"I-ORG",
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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：部分内容因展示需要已被截断。*
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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 fin_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=['fin_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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``` |