## 模型卡
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metadata:
  language: multilingual # AutoModel 是一个支持多语言处理的多模态模型
  license: 
    - apache-2.0 
    - MIT # Apache 2.0 和 MIT 是开源许可
  library_name: pytorch  # 该模型基于 PyTorch 构建
  tags: 
    - multimodal  # 该模型是多模态模型
    - image  # 处理图像任务
    - text  # 处理文本任务
    - audio  # 处理语音任务
    - vqa  # 支持视觉问答任务
    - automatspeerecognition  # 支持自动语音识别任务
    - retrieval  # 支持信息检索任务
datasets:
  - synthetdataset  # 训练和验证使用了合成的多模态数据集
metrics:
  - accuracy  # 视觉问答任务的准确率
  - bleu  # 生成式任务（如字幕生成）的 BLEU 指标
  - wer  # 语音识别任务的 WER（Word Error Rate）
base_model: None  # 该模型为独立设计，没有基于预训练模型
widget:
  - text: "A cat playing with a ball"
    example_title: "Cat"
  - text: "A dog jumping over a fence"
    example_title: "Dog"

model_index:
  - name: AutoModel
    results: 
      - task:
          type: vqa  # 支持视觉问答任务
          name: Visual Question Answering
        dataset:
          type: synthetdataset
          name: Synthetic Multimodal Dataset
          config: default
          split: test
          revision: main
        metrics:
          - type: accuracy
            value: 85.0
            name: VQA Accuracy
      - task:
          type: automatspeerecognition
          name: Automatic Speech Recognition
        dataset:
          type: synthetdataset
          name: Synthetic Multimodal Dataset
          config: default
          split: test
          revision: main
        metrics:
          - type: wer
            value: 15.3
            name: Test WER
      - task:
          type: captioning
          name: Image Captioning
        dataset:
          type: synthetdataset
          name: Synthetic Multimodal Dataset
          config: default
          split: test
          revision: main
        metrics:
          - type: bleu
            value: 27.5
            name: BL4
-----------------------------------------------------------



### **3. 提供可下载文件**
确保以下文件已上传到仓库，便于用户下载和运行：
- **模型权重文件**（如 `AutoModel.pth`）。
- **配置文件**（如 `config.json`）。
- **依赖文件**（如 `requirements.txt`）。
- **运行脚本**（如 `run_model.py`）。
widget:
- text: "Jens Peter Hansen kommer fra Danmark"
用户可以直接下载这些文件并运行模型。

---

### **4. 自动运行模型的限制**
Hugging Face Hub 本身不能自动运行上传的模型，但通过 `Spaces` 提供的接口可以解决这一问题。`Spaces` 能够运行托管的推理服务，让用户无需本地配置即可测试模型。

---

### **推荐方法**
- **快速测试**：使用 Hugging Face `Spaces` 创建在线演示。
- **高级使用**：在模型卡中提供完整的运行说明，允许用户本地运行模型。

通过这些方式，您可以让模型仓库既支持在线运行，也便于用户离线部署。

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## 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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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

**BibTeX:**

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**APA:**

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## Glossary [optional]

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

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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