模型卡
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
Direct Use
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Bias, Risks, and Limitations
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Recommendations
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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Evaluation
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Summary
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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