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kenhktsui/math-classifiers-data | kenhktsui | "2025-02-25T21:39:33Z" | 0 | 0 | [
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suku9/enamine_smiles_twothird_sample | suku9 | "2025-02-25T18:57:38Z" | 0 | 0 | [
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supergoose/flan_source_wiki_bio_key_content_22 | supergoose | "2025-02-25T18:57:50Z" | 0 | 0 | [
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AdvikMehta/qc-sft-tb5-v2 | AdvikMehta | "2025-02-25T18:53:41Z" | 0 | 0 | [
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---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for qc-sft-tb5-v2
This dataset has been created with [distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/AdvikMehta/qc-sft-tb5-v2/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/AdvikMehta/qc-sft-tb5-v2/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: default </summary><hr>
```json
{
"conversations": "[{\"from\": \"system\", \"value\": \"You are a Quality Control Analysis assistant for PCL Industrial Management Inc. (PIMI). Follow these guidelines:\\n\\n1. Answer questions about quality control documents, processes, and standards with accurate technical information\\n2. Use precise technical terminology where appropriate\\n3. Keep responses focused and concise - include only relevant information\\n4. If the provided context doesn\u0027t contain information needed to answer the question:\\n - Clearly state that you don\u0027t have enough information to answer\\n - Don\u0027t attempt to make up or infer information not present in the context\\n5. For general questions that don\u0027t require specific quality control context, provide brief, accurate responses\\n\\nYour primary goal is to provide specific, accurate information based solely on the context provided, without unnecessary elaboration or speculation.\"}, {\"from\": \"human\", \"value\": \"\\nHere is some context from Section 1.0 INTRODUCTION of the Quality Manual for PCL Industrial Management Inc. (PIMI): \\n1.0 INTRODUCTION\\n\\nPCL Industrial Management Inc. (PIMI) is a diversified general industrial construction and management company servicing the oil, gas, power, petrochemical, mining, offshore, and forestry industries throughout Canada. The field operations are supported by the pressure piping and equipment fabrication facilities and module assembly yards in Alberta, along with PCL Industrial Management Inc. district offices.\\n\\nThis quality manual is the primary document for implementing quality management for PCL Industrial Management Inc. and outlines the required procedures to be implemented at PCL Industrial Management Inc. This quality management system (QMS) will be monitored and further documented as required as part of an overall continual improvement process. Quality management implementation and maintenance is the responsibility of all PCL Industrial Management Inc. employees.\\n\\nPCL Industrial Management Inc. is committed to total customer satisfaction. While we continue to develop unique solutions for the needs of the industrial construction industry, we strive to uphold the highest quality practices that enable us to provide our customers with on-time delivery of deficiency-free construction achieved through continuous improvement and technological advances.\\n\\n\\nUser: What are the potential implications for PCL Industrial Management Inc.\u0027s commitment to total customer satisfaction, and how does this relate to the implementation of a quality management system (QMS) that emphasizes continuous improvement and technological advances, considering the diverse range of industries in which they operate?\\n\"}, {\"from\": \"gpt\", \"value\": \"The implementation of a QMS that emphasizes continuous improvement and technological advances may have significant implications for PCL Industrial Management Inc.\u0027s commitment to total customer satisfaction. By prioritizing quality practices, PIMI aims to deliver deficiency-free construction on time, which directly impacts customer satisfaction. The emphasis on continuous improvement and technological advances suggests an ongoing effort to adapt to changing industry demands, ensuring that PIMI\u0027s solutions remain tailored to meet the unique needs of each sector, such as oil, gas, power, and petrochemical industries (Section 1.0 INTRODUCTION). This approach can lead to enhanced customer satisfaction by providing on-time delivery of high-quality products and services.\"}]"
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("AdvikMehta/qc-sft-tb5-v2", "default")
```
Or simply as it follows, since there's only one configuration and is named `default`:
```python
from datasets import load_dataset
ds = load_dataset("AdvikMehta/qc-sft-tb5-v2")
```
</details>
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---
|
supergoose/flan_source_dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to_30 | supergoose | "2025-02-25T19:10:00Z" | 0 | 0 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-02-25T19:09:37Z" | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: _template_idx
dtype: int64
- name: _task_source
dtype: string
- name: _task_name
dtype: string
- name: _template_type
dtype: string
splits:
- name: train
num_bytes: 1308300697
num_examples: 1000000
download_size: 171233165
dataset_size: 1308300697
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
amuvarma/extra-emo-qas-proc | amuvarma | "2025-02-25T19:09:58Z" | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-02-25T19:09:56Z" | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 40433
num_examples: 75
download_size: 14602
dataset_size: 40433
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
supergoose/flan_source_wiki_dialog_ii_32 | supergoose | "2025-02-25T19:14:45Z" | 0 | 0 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-02-25T19:11:41Z" | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: _template_idx
dtype: int64
- name: _task_source
dtype: string
- name: _task_name
dtype: string
- name: _template_type
dtype: string
splits:
- name: train
num_bytes: 2811820940
num_examples: 1000000
download_size: 1497634872
dataset_size: 2811820940
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
YinghaoHu/wisdomInterrogatory-R1 | YinghaoHu | "2025-02-25T19:34:32Z" | 0 | 1 | [
"task_categories:question-answering",
"language:zh",
"license:apache-2.0",
"size_categories:10K<n<100K",
"region:us",
"legal"
] | [
"question-answering"
] | "2025-02-25T19:12:08Z" | ---
license: apache-2.0
task_categories:
- question-answering
language:
- zh
tags:
- legal
pretty_name: wisdomInterrogatory-R1
size_categories:
- 10K<n<100K
---
#### 智海-录问 推理数据(wisdomInterrogatory-R1)
| 类别 | 数据量 | 任务描述 |
| -------- | ------ | :----------------------------------------------------------- |
| 罪名预测 | 20k | 请作为中国法官,基于案件事实,对被告人进行单一罪名预测。<br />返回格式:“<ans>罪名</ans>”.<br />示例:“<ans>盗窃</ans>”,“<ans>敲诈勒索</ans>”。 |
| 刑期预测 | 5k | 请作为中国法官,基于案件事实,对被告人进行量刑预测,请遵从下列规则,返回结果:<br />- 如果量刑为有期徒刑,请返回:“<ans>量刑月数</ans>”,例如应判决5年,5年=60月,则返回<ans>60</ans>.<br />- 如果量刑为无期徒刑,请返回:“<ans>life_imprisonment</ans>”.<br />- 如果量刑为死刑,请返回:“<ans>death_penalty</ans>”。 |
| 论辩挖掘 | 4k | 在法院的庭审过程中,诉方与辩方由于立场观点或事实陈述的差异,会形成庭审争议焦点,这是整场庭审的核心环节。这种辩方与诉方之间形成的逻辑互动论点对,即为争议焦点。请作为中国的辩护律师,执行“论辩挖掘”任务,即根据提供的被告罪名和诉方论点,从五个候选辩护论点中,选择一个最适合作为与诉方观点形成互动对的论点。需要特别说明的是,争议焦点的对抗,始终基于事实基础。<br />返回格式:“<ans>编号</ans>”.<br />示例:“<ans>1</ans> |
| 多跳任务 | 14k | 请你作为一个法律专家,对于提供的司法考试题面以及选项,输出题目的答案。所有的试题均为不定项选择题,选择出一个或者多个符合题面的选项。<br />返回格式:“<ans>选项1,...,选项n</ans>”。如果有多个选项,选项间用,分隔。<br />示例:“<ans>A</ans>”,“<ans>B,C</ans>”。 |
| 数值计算 | 1.4k | 你作为一个中国的法律专家,请基于提供的案件事实描述,依据中国法律的相关条款,计算涉案金额。涉案金额保留两位小数,并以人民币为单位。<br />返回格式:“<ans>涉案金额</ans>”.<br />示例:“<ans>3900.00</ans>”. |
#### 罪名预测-单人单罪
```json
{
"instruction": "请作为中国法官,基于案件事实,对被告人进行单一罪名预测。\n返回格式:“<ans>罪名</ans>”.\n示例:“<ans>盗窃</ans>”,“<ans>敲诈勒索</ans>”.",
"query": "山东省肥城市人民检察院指控,2018年2月1日21时许,被告人李某酒后驾驶鲁J×××××号小型面包车沿朝阳路由西向东行至泰肥一级路北仪阳街道办事处工业园橡胶厂门口路段时,被执勤民警当场查获。经抽血鉴定,被告人李某血液中酒精含量为211.9mg/100ml。\n",
"criminals": [
"李某"
],
"accusation_tag": [
"危险驾驶"
],
"user_input": "请作为中国法官,基于案件事实,对被告人进行单一罪名预测。\n返回格式:“<ans>罪名</ans>”.\n示例:“<ans>盗窃</ans>”,“<ans>敲诈勒索</ans>”.\n被告人为:['李某']\n山东省肥城市人民检察院指控,2018年2月1日21时许,被告人李某酒后驾驶鲁J×××××号小型面包车沿朝阳路由西向东行至泰肥一级路北仪阳街道办事处工业园橡胶厂门口路段时,被执勤民警当场查获。经抽血鉴定,被告人李某血液中酒精含量为211.9mg/100ml。\n",
"llm_output": "<think>\n好的,我现在需要处理这个案件,预测被告人的罪名。首先,案件事实是李某在2018年2月酒后驾驶被查获,血液酒精含量211.9mg/100ml。根据中国的刑法,酒后驾驶涉及危险驾驶罪。危险驾驶罪的构成要件包括在道路上醉酒驾驶机动车,而酒精含量超过80mg/100ml就属于醉酒。李某的数值明显超过标准,且驾驶的是小型面包车,属于机动车。因此,应该认定为危险驾驶罪。\n</think>\n\n<ans>危险驾驶</ans>",
"DS-R1-COT": "好的,我现在需要处理这个案件,预测被告人的罪名。首先,案件事实是李某在2018年2月酒后驾驶被查获,血液酒精含量211.9mg/100ml。根据中国的刑法,酒后驾驶涉及危险驾驶罪。危险驾驶罪的构成要件包括在道路上醉酒驾驶机动车,而酒精含量超过80mg/100ml就属于醉酒。李某的数值明显超过标准,且驾驶的是小型面包车,属于机动车。因此,应该认定为危险驾驶罪。",
"DS-R1-ANS": "危险驾驶"
}
```
- instruction(str)为任务描述提示词
- query(str)为案件事实
- criminals(list)为被告人,且list长度为1
- accusation_tag(list)为被告人的罪名的参考答案,且list长度为1
- user_input(str)为组合好的用于训练的输入
- llm_output(str)为组合好的用于训练的输出,里面包含思维过程和答案
- DS-R1-COT(str)为利用满血R1拒绝采样的思维过程,且通过基于规则的检查手段
- DS-R1-ANS(str)为利用满血R1拒绝采样的答案部分,确保和参考答案一致
#### 刑期预测
```json
{
"instruction": "请作为中国法官,基于案件事实,对被告人进行量刑预测,\n请遵从下列规则,返回结果:- 如果量刑为有期徒刑,请返回:“<ans>量刑月数</ans>”,例如应判决5年,5年=60月,则返回<ans>60</ans>.- 如果量刑为无期徒刑,请返回:“<ans>life_imprisonment</ans>”.- 如果量刑为死刑,请返回:“<ans>death_penalty</ans>”.",
"query": "经审理查明,2018年1月20日13时50分许,被告人王某某驾驶豫D-×××××号飞碟牌中型普通货车沿宝郏公路由东向西行驶至宝丰县石桥镇寺门村西路段时,与路边临时停车由李某2驾驶的华威牌电动三轮车发生交通事故,造成李某2、华威牌电动三轮车乘坐人曹某、李1某受伤,两车不同程度损坏。被害人李某2经抢救无效于2018年1月21日死亡。经鉴定,被害人李某2的死亡符合交通伤所致的重度颅脑损伤合并胸部闭合性损伤;被害人曹某损伤程度查时属轻伤一级。经宝丰县公安交通警察大队认定,被告人王某某负事故的全部责任。事故发生后,被告人王某某在事故现场等候处理,先后拨打110报警、120急救电话。2018年1月26日,被告人王某某到宝丰县公安局投案。\n另查明,事故发生后,被告人王某某为被害人垫付医疗费45000元。2018年5月29日,被告人王某某就民事赔偿与被害人近亲属达成调解协议,赔偿了被害人近亲属并取得了谅解。\n上述事实,被告人王某某在开庭审理过程中亦无异议,且有被告人王某某的供述,证人王某、李某1的证言,被害人曹某的陈述、尸体检验报告书、伤情鉴定意见书、照片及鉴定资质,事故车辆痕迹检验笔录及照片,道路交通事故现场勘查笔录、交通事故现场图及现场照片,户籍证明、前科证明、接处警信息表、驾驶证、行驶证及身份证复印件、道路交通事故认定书,发破案经过、抓获经过等证据证实,足以认定。\n",
"criminals": [
"王某某"
],
"death_penalty_tag": false,
"life_imprisonment_tag": false,
"imprisonment_tag": 24,
"user_input": "请作为中国法官,基于案件事实,对被告人进行量刑预测,\n请遵从下列规则,返回结果:- 如果量刑为有期徒刑,请返回:“<ans>量刑月数</ans>”,例如应判决5年,5年=60月,则返回<ans>60</ans>.- 如果量刑为无期徒刑,请返回:“<ans>life_imprisonment</ans>”.- 如果量刑为死刑,请返回:“<ans>death_penalty</ans>”.\n被告人为:['王某某']\n经审理查明,2018年1月20日13时50分许,被告人王某某驾驶豫D-×××××号飞碟牌中型普通货车沿宝郏公路由东向西行驶至宝丰县石桥镇寺门村西路段时,与路边临时停车由李某2驾驶的华威牌电动三轮车发生交通事故,造成李某2、华威牌电动三轮车乘坐人曹某、李1某受伤,两车不同程度损坏。被害人李某2经抢救无效于2018年1月21日死亡。经鉴定,被害人李某2的死亡符合交通伤所致的重度颅脑损伤合并胸部闭合性损伤;被害人曹某损伤程度查时属轻伤一级。经宝丰县公安交通警察大队认定,被告人王某某负事故的全部责任。事故发生后,被告人王某某在事故现场等候处理,先后拨打110报警、120急救电话。2018年1月26日,被告人王某某到宝丰县公安局投案。\n另查明,事故发生后,被告人王某某为被害人垫付医疗费45000元。2018年5月29日,被告人王某某就民事赔偿与被害人近亲属达成调解协议,赔偿了被害人近亲属并取得了谅解。\n上述事实,被告人王某某在开庭审理过程中亦无异议,且有被告人王某某的供述,证人王某、李某1的证言,被害人曹某的陈述、尸体检验报告书、伤情鉴定意见书、照片及鉴定资质,事故车辆痕迹检验笔录及照片,道路交通事故现场勘查笔录、交通事故现场图及现场照片,户籍证明、前科证明、接处警信息表、驾驶证、行驶证及身份证复印件、道路交通事故认定书,发破案经过、抓获经过等证据证实,足以认定。\n",
"llm_output": "<think>\n好的,我现在需要作为中国法官对被告人王某某进行量刑预测。首先,我得仔细阅读案件事实,了解案情。根据描述,2018年1月20日,王某某驾驶货车与李某2驾驶的电动三轮车发生交通事故,导致李某2死亡,曹某轻伤一级,李1某受伤。交警认定王某某负全责。事故发生后,王某某留在现场报警并拨打急救电话,随后投案自首。此外,王某某垫付了部分医疗费,并与被害人家属达成调解协议,赔偿并获得谅解。\n\n接下来要考虑中国的相关法律规定。根据《中华人民共和国刑法》第一百三十三条,交通肇事罪的处理分为不同情况。如果造成一人死亡或三人重伤,负事故全部或主要责任,处三年以下有期徒刑或拘役。如果有逃逸或其他恶劣情节,刑期会加重。但本案中,王某某并没有逃逸,反而留在现场报警,属于自首,依法可以从轻或减轻处罚。另外,他积极赔偿并获得谅解,这通常也是从轻处罚的情节。\n\n根据最高人民法院关于审理交通肇事刑事案件具体应用法律若干问题的解释,负事故全责导致一人死亡,已经构成交通肇事罪,基准刑可能在三年以下。自首可以减少基准刑的10%-40%,赔偿并取得谅解可以减少基准刑的20%以下。因此,综合这些因素,实际量刑可能会在两年左右,考虑到自首和赔偿谅解,可能还会进一步减轻。但需要确认是否有其他加重或减轻情节。例如,是否酒驾、毒驾或者有其他恶劣情节,但案情中没有提到这些,所以假设不存在。\n\n最终,可能判处有期徒刑两年以下,比如1年6个月到2年之间,转换为月数就是18到24个月。考虑到王某某的积极表现,可能接近18个月。但需要参考类似案例的量刑情况。比如,类似的自首加赔偿谅解的案件,通常会在1年半左右。因此,可能判决18个月左右。需确认是否符合法律规定和司法解释,确保不超出范围。\n</think>\n\n<ans>18</ans>",
"DS-R1-COT": "好的,我现在需要作为中国法官对被告人王某某进行量刑预测。首先,我得仔细阅读案件事实,了解案情。根据描述,2018年1月20日,王某某驾驶货车与李某2驾驶的电动三轮车发生交通事故,导致李某2死亡,曹某轻伤一级,李1某受伤。交警认定王某某负全责。事故发生后,王某某留在现场报警并拨打急救电话,随后投案自首。此外,王某某垫付了部分医疗费,并与被害人家属达成调解协议,赔偿并获得谅解。\n\n接下来要考虑中国的相关法律规定。根据《中华人民共和国刑法》第一百三十三条,交通肇事罪的处理分为不同情况。如果造成一人死亡或三人重伤,负事故全部或主要责任,处三年以下有期徒刑或拘役。如果有逃逸或其他恶劣情节,刑期会加重。但本案中,王某某并没有逃逸,反而留在现场报警,属于自首,依法可以从轻或减轻处罚。另外,他积极赔偿并获得谅解,这通常也是从轻处罚的情节。\n\n根据最高人民法院关于审理交通肇事刑事案件具体应用法律若干问题的解释,负事故全责导致一人死亡,已经构成交通肇事罪,基准刑可能在三年以下。自首可以减少基准刑的10%-40%,赔偿并取得谅解可以减少基准刑的20%以下。因此,综合这些因素,实际量刑可能会在两年左右,考虑到自首和赔偿谅解,可能还会进一步减轻。但需要确认是否有其他加重或减轻情节。例如,是否酒驾、毒驾或者有其他恶劣情节,但案情中没有提到这些,所以假设不存在。\n\n最终,可能判处有期徒刑两年以下,比如1年6个月到2年之间,转换为月数就是18到24个月。考虑到王某某的积极表现,可能接近18个月。但需要参考类似案例的量刑情况。比如,类似的自首加赔偿谅解的案件,通常会在1年半左右。因此,可能判决18个月左右。需确认是否符合法律规定和司法解释,确保不超出范围。",
"DS-R1-ANS": "18"
}
```
- instruction(str)为任务描述提示词
- query(str)为案件事实
- criminals(list)为被告人,且list长度为1
- death_penalty_tag(bool)为是否死刑的标签,true为死刑,false为非死刑
- life_imprisonment_tag(bool)为是否无期徒刑的标签,true为无期徒刑,false为非无期徒刑
- imprisonment_tag(int)为有期徒刑,默认单位是月
- user_input(str)为组合好的用于训练的输入
- llm_output(str)为组合好的用于训练的输出,里面包含思维过程和答案
- DS-R1-COT(str)为利用满血R1拒绝采样的思维过程,且通过基于规则的检查手段
- DS-R1-ANS(str)为利用满血R1拒绝采样的答案部分,确保和参考答案一致
#### 论辩挖掘
```json
{
"instruction": "在法院的庭审过程中,诉方与辩方由于立场观点或事实陈述的差异,会形成庭审争议焦点,这是整场庭审的核心环节。这种辩方与诉方之间形成的逻辑互动论点对,即为争议焦点。请作为中国的辩护律师,执行“论辩挖掘”任务,即根据提供的被告罪名和诉方论点,从五个候选辩护论点中,选择一个最适合作为与诉方观点形成互动对的论点。需要特别说明的是,争议焦点的对抗,始终基于事实基础。",
"category": "刑事",
"crime": "交通肇事罪",
"sc": "上述损失要求被告中国人民财产保险股份有限公司武隆支公司在保险的责任限额内先进赔偿,不足部分要求被告人杨某及被告李某某共同赔偿,相互承担连带责任。",
"bc_1": "六、附带民事部分:对于二位附带民事诉讼原告人的损失,医疗费以医院的单据认定;护理费、营养费按法律规定赔偿;交通费应以实际发生的票据认定;鉴定费没有异议;残疾补偿金及精神损害抚慰金不予支持;关于整容费没有实际发生,不予赔偿。",
"bc_2": "民事部份愿意赔偿。",
"bc_3": "附带民事诉讼被告人财保敖汉支公司辩称,我公司同意在保险限额范围内赔偿附带民事原告人的各项损失,在一审判决后,我公司已经将应当赔付的交强险110000元和商业险500000元划拨到法院执行局账户。",
"bc_4": "而道路交通事故认定书却认定被告人石某鸿负全部责任,也是显失公平的;敖汉旗交警队在制作事故认定书过程中,应该召集各方当事人到场,公开调查取得的证据,而交警部门忽视了以上两位死者的违法行为,是错误的;孔某平的摩托车未投保交强险,应属于不允许上路行驶的机动车,故孔某平存在严重过错和严重的违法行为。",
"bc_5": "两位死者违反强制性法律规定,在车把上悬挂装满水果的塑料袋,导致摩托车向悬挂水果的一侧受力倾斜。",
"answer": 2,
"user_input": "在法院的庭审过程中,诉方与辩方由于立场观点或事实陈述的差异,会形成庭审争议焦点,这是整场庭审的核心环节。这种辩方与诉方之间形成的逻辑互动论点对,即为争议焦点。请作为中国的辩护律师,执行“论辩挖掘”任务,即根据提供的被告罪名和诉方论点,从五个候选辩护论点中,选择一个最适合作为与诉方观点形成互动对的论点。需要特别说明的是,争议焦点的对抗,始终基于事实基础。\n以下是你需要完成的任务的相关信息:被告罪名:交通肇事罪\n诉方论点:上述损失要求被告中国人民财产保险股份有限公司武隆支公司在保险的责任限额内先进赔偿,不足部分要求被告人杨某及被告李某某共同赔偿,相互承担连带责任。\n候选的辩护方论点:(1)六、附带民事部分:对于二位附带民事诉讼原告人的损失,医疗费以医院的单据认定;护理费、营养费按法律规定赔偿;交通费应以实际发生的票据认定;鉴定费没有异议;残疾补偿金及精神损害抚慰金不予支持;关于整容费没有实际发生,不予赔偿。\n(2)民事部份愿意赔偿。\n(3)附带民事诉讼被告人财保敖汉支公司辩称,我公司同意在保险限额范围内赔偿附带民事原告人的各项损失,在一审判决后,我公司已经将应当赔付的交强险110000元和商业险500000元划拨到法院执行局账户。\n(4)而道路交通事故认定书却认定被告人石某鸿负全部责任,也是显失公平的;敖汉旗交警队在制作事故认定书过程中,应该召集各方当事人到场,公开调查取得的证据,而交警部门忽视了以上两位死者的违法行为,是错误的;孔某平的摩托车未投保交强险,应属于不允许上路行驶的机动车,故孔某平存在严重过错和严重的违法行为。\n(5)两位死者违反强制性法律规定,在车把上悬挂装满水果的塑料袋,导致摩托车向悬挂水果的一侧受力倾斜。\n返回格式:“<ans>编号</ans>”.\n示例:“<ans>1</ans>",
"llm_output": "<think>\n好的,我现在需要处理这个论辩挖掘任务。首先,被告的罪名是交通肇事罪,诉方的论点是要求保险公司在责任限额内先行赔偿,不足部分由杨某和李某某共同承担连带责任。我的任务是从五个候选辩护论点中选出一个最适合与诉方论点形成争议焦点的论点。\n\n首先,我需要明确争议焦点的定义:双方在事实或法律适用上的对抗性论点。因此,应该选择能够直接针对诉方主张的辩护论点,尤其是与责任分配和赔偿义务相关的点。\n\n分析各个候选论点:\n\n1. 选项1详细列出了对各项赔偿项目的异议,但主要是对赔偿金额的具体计算提出异议,没有直接涉及责任归属或连带责任的问题。这可能更多是赔偿数额的争议,而非责任主体的争议。\n\n2. 选项2“民事部分愿意赔偿”过于笼统,没有具体对抗点,无法形成有效的争议焦点。\n\n3. 选项3提到保险公司已经赔付了交强险和商业险,这可能用来主张保险公司已经履行了义务,不足部分的责任可能需要重新考虑。但诉方论点中已经提到保险公司在责任限额内先行赔偿,所以这里的赔付可能已经被诉方考虑进去,这个论点可能无法有效对抗诉方关于连带责任的诉求。\n\n4. 选项4质疑交通事故认定书的责任划分,指出交警部门未充分考虑其他当事人的违法行为,如孔某平摩托车未投保交强险等。这直接挑战了事故责任认定的基础,如果责任认定被推翻,可能会影响连带责任的分配,因为原责任认定是诉方主张的基础。这涉及到事实认定的争议,符合基于事实的对抗。\n\n5. 选项5指出死者自身存在违法行为(悬挂重物导致车辆倾斜),这属于受害人过错,可能减轻或免除被告的责任。这也属于事实层面的争议,能够直接对抗诉方的赔偿请求,尤其是连带责任部分。\n\n现在需要比较选项4和选项5。诉方的论点是基于被告和保险公司应赔偿,而选项4挑战的是整个事故责任认定,如果责任认定错误,那么被告的责任可能被重新评估,连带责任的基础可能被动摇。选项5则指出受害人自身过错,可能减轻被告责任。两者都可能有效,但需要看哪个更直接对抗诉方论点。\n\n诉方主张的是保险公司先行赔偿,不足部分由被告共同赔偿。共同赔偿的前提是被告的责任成立。选项4挑战的是责任认定的正确性,如果责任认定被推翻,被告可能不承担或承担较少责任,直接影响连带责任的基础。而选项5则是部分减轻责任,可能影响赔偿比例,但可能不直接动摇连带责任的基础。因此,选项4更可能形成争议焦点,因为它直接攻击责任认定的合法性,而责任认定是诉方主张的基础。\n</think>\n\n<ans>4</ans>",
"DS-R1-COT": "好的,我现在需要处理这个论辩挖掘任务。首先,被告的罪名是交通肇事罪,诉方的论点是要求保险公司在责任限额内先行赔偿,不足部分由杨某和李某某共同承担连带责任。我的任务是从五个候选辩护论点中选出一个最适合与诉方论点形成争议焦点的论点。\n\n首先,我需要明确争议焦点的定义:双方在事实或法律适用上的对抗性论点。因此,应该选择能够直接针对诉方主张的辩护论点,尤其是与责任分配和赔偿义务相关的点。\n\n分析各个候选论点:\n\n1. 选项1详细列出了对各项赔偿项目的异议,但主要是对赔偿金额的具体计算提出异议,没有直接涉及责任归属或连带责任的问题。这可能更多是赔偿数额的争议,而非责任主体的争议。\n\n2. 选项2“民事部分愿意赔偿”过于笼统,没有具体对抗点,无法形成有效的争议焦点。\n\n3. 选项3提到保险公司已经赔付了交强险和商业险,这可能用来主张保险公司已经履行了义务,不足部分的责任可能需要重新考虑。但诉方论点中已经提到保险公司在责任限额内先行赔偿,所以这里的赔付可能已经被诉方考虑进去,这个论点可能无法有效对抗诉方关于连带责任的诉求。\n\n4. 选项4质疑交通事故认定书的责任划分,指出交警部门未充分考虑其他当事人的违法行为,如孔某平摩托车未投保交强险等。这直接挑战了事故责任认定的基础,如果责任认定被推翻,可能会影响连带责任的分配,因为原责任认定是诉方主张的基础。这涉及到事实认定的争议,符合基于事实的对抗。\n\n5. 选项5指出死者自身存在违法行为(悬挂重物导致车辆倾斜),这属于受害人过错,可能减轻或免除被告的责任。这也属于事实层面的争议,能够直接对抗诉方的赔偿请求,尤其是连带责任部分。\n\n现在需要比较选项4和选项5。诉方的论点是基于被告和保险公司应赔偿,而选项4挑战的是整个事故责任认定,如果责任认定错误,那么被告的责任可能被重新评估,连带责任的基础可能被动摇。选项5则指出受害人自身过错,可能减轻被告责任。两者都可能有效,但需要看哪个更直接对抗诉方论点。\n\n诉方主张的是保险公司先行赔偿,不足部分由被告共同赔偿。共同赔偿的前提是被告的责任成立。选项4挑战的是责任认定的正确性,如果责任认定被推翻,被告可能不承担或承担较少责任,直接影响连带责任的基础。而选项5则是部分减轻责任,可能影响赔偿比例,但可能不直接动摇连带责任的基础。因此,选项4更可能形成争议焦点,因为它直接攻击责任认定的合法性,而责任认定是诉方主张的基础。",
"DS-R1-ANS": "4"
}
```
- instruction(str)为任务描述提示词
- category(str)为案件类型(刑事或者民事)
- crime(str)为被告罪名
- sc(str)为诉方论点
- bc_1-bc_5(str)为候选的辩护方论点
- Answer(int)为辩护方论点参考答案
- user_input(str)为组合好的用于训练的输入
- llm_output(str)为组合好的用于训练的输出,里面包含思维过程和答案
- DS-R1-COT(str)为利用满血R1拒绝采样的思维过程,且通过基于规则的检查手段
- DS-R1-ANS(str)为利用满血R1拒绝采样的答案部分,确保和参考答案一致
#### 多跳任务
```json
{
"instruction": [
"请你作为一个法律专家,对于提供的司法考试题面以及选项,输出题目的答案。所有的试题均为不定项选择题,选择出一个或者多个符合题面的选项。返回格式:“<ans>选项1,...,选项n</ans>”。如果有多个选项,选项间用,分隔。示例:“<ans>A</ans>”,“<ans>B,C</ans>”。"
],
"statement": "关于法律论证中的内部证成和外部证成,下列哪些选项是错误的?",
"option_list": {
"A": "法律论证中的内部证成和外部证成之间的区别表现为,内部证成是针对案件事实问题进行的论证,外部证成是针对法律规范问题进行的论证",
"B": "无论内部证成还是外部证成都不解决法律决定的前提是否正确的问题",
"C": "内部证成主要使用演绎方法,外部证成主要使用归纳方法",
"D": "无论内部证成还是外部证成都离不开支持性理由和推理规则"
},
"answer": [
"A",
"B",
"C"
],
"user_input": "['请你作为一个法律专家,对于提供的司法考试题面以及选项,输出题目的答案。所有的试题均为不定项选择题,选择出一个或者多个符合题面的选项。返回格式:“<ans>选项1,...,选项n</ans>”。如果有多个选项,选项间用,分隔。示例:“<ans>A</ans>”,“<ans>B,C</ans>”。']\n司法考试题面:关于法律论证中的内部证成和外部证成,下列哪些选项是错误的?\n选项:{'A': '法律论证中的内部证成和外部证成之间的区别表现为,内部证成是针对案件事实问题进行的论证,外部证成是针对法律规范问题进行的论证', 'B': '无论内部证成还是外部证成都不解决法律决定的前提是否正确的问题', 'C': '内部证成主要使用演绎方法,外部证成主要使用归纳方法', 'D': '无论内部证成还是外部证成都离不开支持性理由和推理规则'}",
"llm_output": "<think>\n好的,我现在需要分析这道关于法律论证中内部证成和外部证成的多选题。题目要求找出错误的选项,所以我要先回顾相关的法律论证理论,特别是内部证成和外部证成的区别和各自的特点。\n\n首先,题目中的选项A说内部证成针对案件事实问题,外部证成针对法律规范问题。根据阿列克西的法律论证理论,内部证成处理的是法律决定是否从前提中逻辑地推导出来,而外部证成则关注前提本身的正确性。也就是说,内部证成是关于推理过程的正确性,而外部证成是关于前提(如法律规范、事实认定)的正确性。因此,A选项的描述是错误的,因为内部证成和外部证成的区分不在于事实与规范,而是推理过程与前提的正确性。\n\n接下来看选项B,它声称无论内部还是外部证成都不解决前提正确的问题。这里有问题,因为外部证成的核心任务正是解决法律决定的前提是否正确,比如法律规范的有效性、事实的认定等。所以B选项是错误的,外部证成恰恰要解决前提的正确性问题。\n\n选项C提到内部证成用演绎,外部用归纳。实际上,内部证成确实主要使用演绎推理,从前提推导结论。但外部证成并不限于归纳方法,它可能使用多种方法,包括演绎、类比等,来论证前提的正确性。所以C选项的描述是错误的,外部证成不一定主要用归纳。\n\n选项D指出无论哪种证成都需要支持性理由和推理规则。这是正确的,因为无论是内部还是外部证成,都需要合理的理由和遵循逻辑规则来确保论证的有效性。因此D选项正确,不是错误选项。\n\n总结错误选项:A、B、C都是错误的,而D正确。所以正确的答案应选A、B、C。\n</think>\n\n<ans>A,B,C</ans>",
"DS-R1-COT": "好的,我现在需要分析这道关于法律论证中内部证成和外部证成的多选题。题目要求找出错误的选项,所以我要先回顾相关的法律论证理论,特别是内部证成和外部证成的区别和各自的特点。\n\n首先,题目中的选项A说内部证成针对案件事实问题,外部证成针对法律规范问题。根据阿列克西的法律论证理论,内部证成处理的是法律决定是否从前提中逻辑地推导出来,而外部证成则关注前提本身的正确性。也就是说,内部证成是关于推理过程的正确性,而外部证成是关于前提(如法律规范、事实认定)的正确性。因此,A选项的描述是错误的,因为内部证成和外部证成的区分不在于事实与规范,而是推理过程与前提的正确性。\n\n接下来看选项B,它声称无论内部还是外部证成都不解决前提正确的问题。这里有问题,因为外部证成的核心任务正是解决法律决定的前提是否正确,比如法律规范的有效性、事实的认定等。所以B选项是错误的,外部证成恰恰要解决前提的正确性问题。\n\n选项C提到内部证成用演绎,外部用归纳。实际上,内部证成确实主要使用演绎推理,从前提推导结论。但外部证成并不限于归纳方法,它可能使用多种方法,包括演绎、类比等,来论证前提的正确性。所以C选项的描述是错误的,外部证成不一定主要用归纳。\n\n选项D指出无论哪种证成都需要支持性理由和推理规则。这是正确的,因为无论是内部还是外部证成,都需要合理的理由和遵循逻辑规则来确保论证的有效性。因此D选项正确,不是错误选项。\n\n总结错误选项:A、B、C都是错误的,而D正确。所以正确的答案应选A、B、C。",
"DS-R1-ANS": "A,B,C"
}
```
- instruction(list)为任务描述提示词
- statement(str)为司法考试题面
- option_list(dict)为题目对应选项
- answer(list)为题目对应的答案
- user_input(str)为组合好的用于训练的输入
- llm_output(str)为组合好的用于训练的输出,里面包含思维过程和答案
- DS-R1-COT(str)为利用满血R1拒绝采样的思维过程,且通过基于规则的检查手段
- DS-R1-ANS(str)为利用满血R1拒绝采样的答案部分,确保和参考答案一致
#### 数值计算
```json
{
"instruction": "你作为一个中国的法律专家,请基于提供的案件事实描述,依据中国法律的相关条款,计算涉案金额。涉案金额保留两位小数,并以人民币为单位。\n返回格式:“<ans>涉案金额</ans>”.\n示例:“<ans>3900.00</ans>”.",
"justice": "经审理查明,2014年3月期间,被告人韦某伙同韦某甲(已被判刑)共同作案盗窃多起,所盗财物价值共计1858元,其具体盗窃事实如下:\n1.2014年3月23日12时许,被告人韦某伙同韦某甲在柳城县寨隆镇鸡楼村六岭屯被害人韦某乙房屋门前的一块空地上,将韦某乙的两只焦鸭盗走,尔后又到被害人韦某丙的牛X,把韦某丙的一只公鸡及两只母鸡盗走。盗得鸡、鸭后,被告人韦某与韦某甲将其中的一只鸡宰杀吃掉,另将两只焦鸭和两只鸡卖给他人,得款后用于吸食毒品。经柳城县价格认证中心鉴定,被盗的两只焦鸭价值195元,被盗的一只公鸡及两只母鸡价值108元。\n2.2014年3月24日14时许,被告人韦某伙同韦某甲到柳城县寨隆镇鸡楼村六岭屯被害人韦某丙房屋门口墙边,用木棍把韦某丙的一条大黄狗打死后盗走,尔后将死狗卖给他人,得款后用于吸食毒品。经柳城县价格认证中心鉴定,被盗的大黄狗价值325元。\n3.2014年3月27日凌晨2时许,被告人韦某伙同韦某甲到柳城县寨隆镇鸡楼村六岭屯“飞鼠山”石场附近的被害人韦某丁的果地,将韦某丁种植的205株“沙塘橘”果苗拔起后盗走,尔后将果苗拿到柳城县大埔镇集市销赃,但无人购买,两人遂将果苗拿到柳城县大埔镇大桥桥底烧毁。经柳城县价格认证中心鉴定,被盗的205株果苗价值l230元。\n2014年4月8日,被告人韦某因吸食毒品被公安机关抓获并被处以行政拘留。在行政拘留期间,公安机关通过特情人员掌握韦某的盗窃事实后,于2014年4月14日对其刑事拘留。被害人韦某乙、韦某丙、韦某丁的财物因被告人韦某及其同伙韦某甲盗窃造成的经济损失,至今未获赔偿。\n",
"money": "1858.0",
"user_input": "你作为一个中国的法律专家,请基于提供的案件事实描述,依据中国法律的相关条款,计算涉案金额。涉案金额保留两位小数,并以人民币为单位。\n返回格式:“<ans>涉案金额</ans>”.\n示例:“<ans>3900.00</ans>”.\n案件事实描述:经审理查明,2014年3月期间,被告人韦某伙同韦某甲(已被判刑)共同作案盗窃多起,所盗财物价值共计1858元,其具体盗窃事实如下:\n1.2014年3月23日12时许,被告人韦某伙同韦某甲在柳城县寨隆镇鸡楼村六岭屯被害人韦某乙房屋门前的一块空地上,将韦某乙的两只焦鸭盗走,尔后又到被害人韦某丙的牛X,把韦某丙的一只公鸡及两只母鸡盗走。盗得鸡、鸭后,被告人韦某与韦某甲将其中的一只鸡宰杀吃掉,另将两只焦鸭和两只鸡卖给他人,得款后用于吸食毒品。经柳城县价格认证中心鉴定,被盗的两只焦鸭价值195元,被盗的一只公鸡及两只母鸡价值108元。\n2.2014年3月24日14时许,被告人韦某伙同韦某甲到柳城县寨隆镇鸡楼村六岭屯被害人韦某丙房屋门口墙边,用木棍把韦某丙的一条大黄狗打死后盗走,尔后将死狗卖给他人,得款后用于吸食毒品。经柳城县价格认证中心鉴定,被盗的大黄狗价值325元。\n3.2014年3月27日凌晨2时许,被告人韦某伙同韦某甲到柳城县寨隆镇鸡楼村六岭屯“飞鼠山”石场附近的被害人韦某丁的果地,将韦某丁种植的205株“沙塘橘”果苗拔起后盗走,尔后将果苗拿到柳城县大埔镇集市销赃,但无人购买,两人遂将果苗拿到柳城县大埔镇大桥桥底烧毁。经柳城县价格认证中心鉴定,被盗的205株果苗价值l230元。\n2014年4月8日,被告人韦某因吸食毒品被公安机关抓获并被处以行政拘留。在行政拘留期间,公安机关通过特情人员掌握韦某的盗窃事实后,于2014年4月14日对其刑事拘留。被害人韦某乙、韦某丙、韦某丁的财物因被告人韦某及其同伙韦某甲盗窃造成的经济损失,至今未获赔偿。\n",
"llm_output": "<think>\n好的,我需要根据提供的案件事实计算涉案金额。首先,案件中有三起盗窃事实,我需要逐一计算每一起的金额,然后相加总和。\n\n第一起案件发生在2014年3月23日,盗窃的是两只焦鸭和一只公鸡及两只母鸡。根据鉴定,两只焦鸭价值195元,公鸡和两只母鸡价值108元。这里要注意的是,虽然他们吃掉了一只鸡,但盗窃的金额应该按被盗物品的全部价值计算,不管之后如何处理。所以这部分金额是195 + 108 = 303元。\n\n第二起案件在3月24日,盗窃了一条大黄狗,鉴定价值325元。这部分直接加上325元。\n\n第三起案件是3月27日盗窃了205株果苗,价值1230元。注意这里原文中的“价值l230元”可能存在笔误,应该是1230元。所以加上1230元。\n\n接下来,将三部分相加:303 + 325 + 1230 = 1858元。这与案件事实中提到的总金额1858元一致,所以不需要额外调整。最后,金额保留两位小数,即1858.00元人民币。\n</think>\n\n<ans>1858.00</ans>",
"DS-R1-COT": "好的,我需要根据提供的案件事实计算涉案金额。首先,案件中有三起盗窃事实,我需要逐一计算每一起的金额,然后相加总和。\n\n第一起案件发生在2014年3月23日,盗窃的是两只焦鸭和一只公鸡及两只母鸡。根据鉴定,两只焦鸭价值195元,公鸡和两只母鸡价值108元。这里要注意的是,虽然他们吃掉了一只鸡,但盗窃的金额应该按被盗物品的全部价值计算,不管之后如何处理。所以这部分金额是195 + 108 = 303元。\n\n第二起案件在3月24日,盗窃了一条大黄狗,鉴定价值325元。这部分直接加上325元。\n\n第三起案件是3月27日盗窃了205株果苗,价值1230元。注意这里原文中的“价值l230元”可能存在笔误,应该是1230元。所以加上1230元。\n\n接下来,将三部分相加:303 + 325 + 1230 = 1858元。这与案件事实中提到的总金额1858元一致,所以不需要额外调整。最后,金额保留两位小数,即1858.00元人民币。",
"DS-R1-ANS": "1858.00"
}
```
- instruction(str)为任务描述提示词
- justice(str)为案件事实描述
- money(str)为涉案金额参考答案
- user_input(str)为组合好的用于训练的输入
- llm_output(str)为组合好的用于训练的输出,里面包含思维过程和答案
- DS-R1-COT(str)为利用满血R1拒绝采样的思维过程,且通过基于规则的检查手段
- DS-R1-ANS(str)为利用满血R1拒绝采样的答案部分,确保和参考答案一致
|
supergoose/flan_source_app_reviews_generate_review_34 | supergoose | "2025-02-25T19:13:05Z" | 0 | 0 | [
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|
supergoose/flan_source_app_reviews_categorize_rating_using_review_35 | supergoose | "2025-02-25T19:13:06Z" | 0 | 0 | [
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|
supergoose/flan_source_app_reviews_convert_to_star_rating_36 | supergoose | "2025-02-25T19:14:07Z" | 0 | 0 | [
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|
supergoose/flan_source_kilt_tasks_hotpotqa_straighforward_qa_48 | supergoose | "2025-02-25T19:13:45Z" | 0 | 0 | [
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|
supergoose/flan_source_app_reviews_convert_to_rating_37 | supergoose | "2025-02-25T19:14:23Z" | 0 | 0 | [
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|
supergoose/flan_source_kilt_tasks_hotpotqa_combining_facts_49 | supergoose | "2025-02-25T19:14:15Z" | 0 | 0 | [
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|
supergoose/flan_source_kilt_tasks_hotpotqa_complex_question_51 | supergoose | "2025-02-25T19:14:32Z" | 0 | 0 | [
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|
supergoose/flan_source_kilt_tasks_hotpotqa_formulate_52 | supergoose | "2025-02-25T19:14:39Z" | 0 | 0 | [
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|
cchoi1/eval-attacker-Qwen2.5-Coder-1.5B-Instruct-dpo-10-epochs_att10_sol10_20250225_104834 | cchoi1 | "2025-02-25T19:14:37Z" | 0 | 0 | [
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|
supergoose/flan_source_kilt_tasks_hotpotqa_final_exam_53 | supergoose | "2025-02-25T19:15:17Z" | 0 | 0 | [
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|
supergoose/flan_source_duorc_ParaphraseRC_title_generation_57 | supergoose | "2025-02-25T19:16:38Z" | 0 | 0 | [
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|
supergoose/flan_source_duorc_ParaphraseRC_decide_worth_it_58 | supergoose | "2025-02-25T19:16:46Z" | 0 | 0 | [
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---
|
amuvarma/hybrid-text-convo-labels | amuvarma | "2025-02-25T19:21:08Z" | 0 | 0 | [
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dataset_info:
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---
|
supergoose/flan_source_duorc_ParaphraseRC_question_answering_59 | supergoose | "2025-02-25T19:17:52Z" | 0 | 0 | [
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dataset_info:
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---
|
davhin/parl-synthetic-queries-v3 | davhin | "2025-02-25T19:16:11Z" | 0 | 0 | [
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supergoose/flan_source_duorc_ParaphraseRC_extract_answer_61 | supergoose | "2025-02-25T19:19:03Z" | 0 | 0 | [
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supergoose/flan_source_duorc_ParaphraseRC_answer_question_62 | supergoose | "2025-02-25T19:19:06Z" | 0 | 0 | [
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supergoose/flan_source_race_high_Select_the_best_answer_no_instructions__66 | supergoose | "2025-02-25T19:19:18Z" | 0 | 0 | [
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Kurt232/OpenR1-Math-Filtered | Kurt232 | "2025-02-25T19:19:31Z" | 0 | 0 | [
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supergoose/flan_source_race_high_Write_a_multi_choice_question_options_given__67 | supergoose | "2025-02-25T19:20:08Z" | 0 | 0 | [
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Ahren09/SciEvo | Ahren09 | "2025-02-25T23:47:09Z" | 0 | 0 | [
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---
# 🎓 SciEvo: A Longitudinal Scientometric Dataset
**SciEvo** is a large-scale dataset that spans over **30 years of academic literature** from arXiv, designed to support scientometric research and the study of scientific knowledge evolution. By providing a comprehensive collection of **over two million publications**, including detailed metadata and citation graphs, SciEvo enables researchers to analyze long-term trends in academic disciplines, citation practices, and interdisciplinary knowledge exchange.
💻 <a href="https://github.com/Ahren09/SciEvo.git">GitHub</a>|🤗 <a href="https://huggingface.co/datasets/Ahren09/SciEvo">HuggingFace</a> | 📊 <a href="https://www.kaggle.com/datasets/ahren09/scievo">Kaggle</a> | 📄 <a href="https://arxiv.org/abs/2410.09510">Paper</a>
## Citation
If you use SciEvo in your research, please cite our work:
```
@article{jin2024scito2m,
title={SciEvo: A 2 Million, 30-Year Cross-disciplinary Dataset for Temporal Scientometric Analysis},
author={Jin, Yiqiao and Xiao, Yijia and Wang, Yiyang and Wang, Jindong},
journal={arXiv:2410.09510},
year={2024}
}
```
## Dataset Features
### Semantic Scholar
- **paperId**: The Semantic Scholar ID for the paper.
- **externalIds**: A dictionary containing other external identifiers such as DOI, PubMed ID, etc.
- **corpusId**: An internal identifier used by Semantic Scholar.
- **publicationVenue**: The name of the journal, conference, or workshop where the paper was published.
- **url**: The URL link to the paper on Semantic Scholar.
- **title**: The title of the paper.
- **abstract**: A summary of the paper’s content.
- **venue**: Another reference to the publication venue, which might contain variations of the `publicationVenue` field.
- **year**: The year the paper was published.
- **referenceCount**: The number of references cited in the paper.
- **citationCount**: The number of times this paper has been cited by others.
- **influentialCitationCount**: The number of citations that are considered "influential" by Semantic Scholar’s algorithms. Check this article: *[What are Highly Influential Citations?](https://www.semanticscholar.org/faq/influential-citations#:~:text=Influential%20citations%20are%20determined%20utilizing,in%20%E2%80%9CIdentifying%20Meaningful%20Citations%E2%80%9D.)*
- **isOpenAccess**: A boolean flag indicating whether the paper is available as open access.
- **openAccessPdf**: The URL link to the open-access PDF version of the paper (if available).
- **fieldsOfStudy**: A list of general research fields to which the paper belongs.
- **s2FieldsOfStudy**: A more granular classification of research fields used by Semantic Scholar.
- **publicationTypes**: The type of publication (e.g., journal article, conference paper, preprint, etc.).
- **semanticScholarPublicationDate**: The official publication date recorded by Semantic Scholar.
- **journal**: The journal where the paper was published (if applicable).
- **citationStyles**: Various citation formats for referencing the paper.
- **authors**: A list of authors who contributed to the paper.
- **arXivPublicationDate**: The date the paper was first published on arXiv (if applicable).
- **arXivId**: The identifier for the paper in the arXiv repository (if available).
### arXiv Paper
The `arxiv_data` dataframe contains the following features:
- **id**: The paper's arXiv ID.
- **title**: The title of the paper.
- **summary**: The abstract or summary of the paper.
- **arxiv_comment**: Comments from the authors, (e.g. `Accepted at ICML 2025`).
- **published**: The date when the paper was first published on arXiv.
- **updated**: The date when the paper was last updated on arXiv.
- **authors**: A list of authors who contributed to the paper.
- **tags**: A set of subject categories associated with the paper, e.g. `cs.AI`
- **tags_cleaned**: Processed or cleaned version of the `tags` field.
- **title_keywords**: Keywords extracted from the title.
- **title_and_abstract_keywords**: Keywords extracted from both the title and abstract.
### Citation Graph (References)
- **arXivId**: The paper's arXiv ID.
- **references**: A list of references cited by the paper, including metadata such as fields of study and citation information.
- **arXivPublicationDate**: The date when the paper was first published on arXiv.
## License
SciEvo is released under the [**Apache 2.0 License**](https://www.apache.org/licenses/LICENSE-2.0).
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supergoose/flan_source_race_high_Select_the_best_answer_68 | supergoose | "2025-02-25T19:20:27Z" | 0 | 0 | [
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supergoose/flan_source_race_high_Read_the_article_and_answer_the_question_no_option__73 | supergoose | "2025-02-25T19:22:36Z" | 0 | 0 | [
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supergoose/flan_source_duorc_SelfRC_answer_question_75 | supergoose | "2025-02-25T19:22:56Z" | 0 | 0 | [
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supergoose/flan_source_duorc_SelfRC_decide_worth_it_76 | supergoose | "2025-02-25T19:23:32Z" | 0 | 0 | [
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vaibhavad/generation-eval-data | vaibhavad | "2025-02-25T19:22:52Z" | 0 | 0 | [
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supergoose/flan_source_duorc_SelfRC_movie_director_77 | supergoose | "2025-02-25T19:24:04Z" | 0 | 0 | [
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supergoose/flan_source_duorc_SelfRC_extract_answer_78 | supergoose | "2025-02-25T19:24:09Z" | 0 | 0 | [
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embodied-ai/piper_shirt_hanging_feb25_a5 | embodied-ai | "2025-02-25T19:24:15Z" | 0 | 0 | [
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license: apache-2.0
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---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
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1
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}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
supergoose/flan_source_duorc_SelfRC_title_generation_79 | supergoose | "2025-02-25T19:24:25Z" | 0 | 0 | [
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supergoose/flan_source_duorc_SelfRC_build_story_around_qa_80 | supergoose | "2025-02-25T19:25:09Z" | 0 | 0 | [
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supergoose/flan_source_duorc_SelfRC_generate_question_81 | supergoose | "2025-02-25T19:25:36Z" | 0 | 0 | [
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supergoose/flan_source_duorc_SelfRC_generate_question_by_answer_82 | supergoose | "2025-02-25T19:26:00Z" | 0 | 0 | [
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supergoose/flan_source_duorc_ParaphraseRC_build_story_around_qa_83 | supergoose | "2025-02-25T19:26:03Z" | 0 | 0 | [
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cchoi1/eval-attacker-Qwen2.5-Coder-1.5B-Instruct-dpo-10-epochs_att1_sol1_20250225_112421 | cchoi1 | "2025-02-25T19:25:11Z" | 0 | 0 | [
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supergoose/flan_source_duorc_ParaphraseRC_generate_question_by_answer_84 | supergoose | "2025-02-25T19:26:20Z" | 0 | 0 | [
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supergoose/flan_source_duorc_ParaphraseRC_generate_question_85 | supergoose | "2025-02-25T19:26:35Z" | 0 | 0 | [
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supergoose/flan_source_social_i_qa_Show_choices_and_generate_index_92 | supergoose | "2025-02-25T19:26:10Z" | 0 | 0 | [
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supergoose/flan_source_social_i_qa_Show_choices_and_generate_answer_94 | supergoose | "2025-02-25T19:26:22Z" | 0 | 0 | [
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supergoose/flan_source_social_i_qa_I_was_wondering_95 | supergoose | "2025-02-25T19:26:22Z" | 0 | 0 | [
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- name: _template_idx
dtype: int64
- name: _task_source
dtype: string
- name: _task_name
dtype: string
- name: _template_type
dtype: string
splits:
- name: train
num_bytes: 732096528
num_examples: 103455
download_size: 414816311
dataset_size: 732096528
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Subsets and Splits