README.md

---
license: apache-2.0
datasets:
- feilongfl/ChineseNewsSummary
language:
- zh
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: summarization
widget:
  - text: "概括新闻\n来源：经济参考报\n　　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%。\n　　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%。\n　　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。\n　　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。\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n .appendQr_wrap{border:1px solid #E6E6E6;padding:8px;}\n .appendQr_normal{float:left;}\n .appendQr_normal img{width:74px;}\n .appendQr_normal_txt{float:left;font-size:20px;line-height:74px;padding-left:20px;color:#333;}\n \n \n \n\n \n\n\n\n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

<!-- Provide a quick summary of what the model is/does. [Optional] -->
Chinese news summary

![example](doc/2024-02-19_05-01.png)

#  Table of Contents

- [Model Card for feilongfl/Mistral-7B-cn_news-v0.1](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Table of Contents](#table-of-contents-1)
- [Model Details](#model-details)
  - [Model Description](#model-description)
- [Uses](#uses)
  - [Direct Use](#direct-use)
  - [Downstream Use [Optional]](#downstream-use-optional)
  - [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
  - [Recommendations](#recommendations)
- [Training Details](#training-details)
  - [Training Data](#training-data)
  - [Training Procedure](#training-procedure)
    - [Preprocessing](#preprocessing)
    - [Speeds, Sizes, Times](#speeds-sizes-times)
- [Evaluation](#evaluation)
  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
    - [Testing Data](#testing-data)
    - [Factors](#factors)
    - [Metrics](#metrics)
  - [Results](#results)
- [Model Examination](#model-examination)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications [optional]](#technical-specifications-optional)
  - [Model Architecture and Objective](#model-architecture-and-objective)
  - [Compute Infrastructure](#compute-infrastructure)
    - [Hardware](#hardware)
    - [Software](#software)
- [Citation](#citation)
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- [Model Card Contact](#model-card-contact)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)


# Model Details

## Model Description

<!-- Provide a longer summary of what this model is/does. -->
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

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

## Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->




## Downstream Use [Optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->




## Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->



# Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.


## Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->





# Training Details

## Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

More information on training data needed


## Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

### Preprocessing

More information needed

### Speeds, Sizes, Times

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

More information needed

# Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

## Testing Data, Factors & Metrics

### Testing Data

<!-- This should link to a Data Card if possible. -->

More information needed


### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

More information needed

### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

More information needed

## Results 

More information needed

# Model Examination

More information needed

# Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** More information needed
- **Hours used:** More information needed
- **Cloud Provider:** More information needed
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed

# Technical Specifications [optional]

## Model Architecture and Objective

More information needed

## Compute Infrastructure

More information needed

### Hardware

More information needed

### Software

More information needed

# Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

More information needed

**APA:**

More information needed

# Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

More information needed

# More Information [optional]

More information needed

# Model Card Authors [optional]

<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->

feilongfl

# Model Card Contact

More information needed

# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

More information needed

</details>