license: apache-2.0
datasets:
- feilongfl/ChineseNewsSummary
language:
- zh
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: summarization
widget:
- text: >-
概括新闻
来源：经济参考报
国家邮政局监测数据显示，今年春节长假期间（2月10日至17日），全国邮政快递业总体运行安全平稳，寄递渠道畅通有序。具体来看，揽投快递包裹超17亿件，其中，揽收快递包裹10.79亿件，日均揽收量与2023年春节假期相比增长145.2%；投递快递包裹6.41亿件，日均投递量与2023年春节假期相比增长82.1%。
国家邮政局相关负责人表示，快递包裹量的良好增长态势，进一步凸显邮政快递业务保通保畅、递送温暖的积极作用，也折射出中国消费新活力。数据显示，自春运开始以来（1月26日至2月17日），全国邮政快递业揽收快递包裹56.55亿件，较2023年春运同期增长30.8%；投递快递包裹59.94亿件，较2023年春运同期增长21%。
上述负责人表示，今年春节期间，特色产品和节庆寄递需求旺盛，农产品继续保持增长态势。邮政管理部门坚决守牢安全生产底线，统筹做好春节期间安全生产和寄递服务保障工作。各寄递企业克服低温雨雪天气带来的不利影响，健全完善工作预案，加强人员、运力与物资储备，切实保障在岗快递员合法权益，全力提升年货寄递服务能力。
随着春节假期的结束，国内主要快递企业陆续结束春节模式，网络运营转为正常模式。分拨中心、网点相继开工，快递小哥、货车司机和客服人员等回到工作岗位，迎战即将到来的返工返学所带来的业务高峰。
.appendQr_wrap{border:1px solid #E6E6E6;padding:8px;}
.appendQr_normal{float:left;}
.appendQr_normal img{width:74px;}
.appendQr_normal_txt{float:left;font-size:20px;line-height:74px;padding-left:20px;color:#333;}
责任编辑：王许宁
output:
text: >-
{"title": "全国邮政快递业总体运行安全平稳，寄递渠道畅通有序", "class": "财经", "summary":
"国家邮政局监测数据显示，今年春节长假期间，全国邮政快递业总体运行安全平稳，寄递渠道畅通有序。揽投快递包裹超17亿件，其中，揽收快递包裹10.79亿件，日均揽收量与2023年春节假期相比增长145.2%；投递快递包裹6.41亿件，日均投递量与2023年春节假期相比增长82.1%。数据显示，自春运开始以来，全国邮政快递业揽收快递包裹56.55亿件，较2023年春运同期增长30.8%；投递快递包裹59.94亿件，较2023年春运同期增长21%。国家邮政局相关负责人表示，快递包裹量的良好增长态势，进一步凸显邮政快递业务保通保畅、递送温暖的积极作用，也折射出中国消费新活力。"}
Model Card for feilongfl/Mistral-7B-cn_news-v0.1
Chinese news summary
Table of Contents
- Model Card for feilongfl/Mistral-7B-cn_news-v0.1
- Table of Contents
- Table of Contents
- Model Details
- Uses
- Bias, Risks, and Limitations
- Training Details
- Evaluation
- Model Examination
- Environmental Impact
- Technical Specifications [optional]
- Citation
- Glossary [optional]
- More Information [optional]
- Model Card Authors [optional]
- Model Card Contact
- How to Get Started with the Model
Model Details
Model Description
Chinese news summary
- Developed by: More information needed
- Shared by [Optional]: More information needed
- Model type: Language model
- Language(s) (NLP): zh
- License: apache-2.0
- Parent Model: More information needed
- Resources for more information: More information needed
Uses
Direct Use
Downstream Use [Optional]
Out-of-Scope Use
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Recommendations
Training Details
Training Data
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Training Procedure
Preprocessing
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Speeds, Sizes, Times
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Model Examination
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
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Citation
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Glossary [optional]
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Model Card Authors [optional]
feilongfl
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How to Get Started with the Model
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