README.md

metadata

language:
  - en
  - zh
license: apache-2.0
library_name: transformers
tags:
  - multimodal
  - vqa
  - text
  - audio
datasets:
  - synthetic-dataset
metrics:
  - accuracy
  - bleu
  - wer
model-index:
  - name: AutoModel
    results:
      - task:
          type: vqa
          name: Visual Question Answering
        dataset:
          type: synthetic-dataset
          name: Synthetic Multimodal Dataset
          split: test
        metrics:
          - type: accuracy
            value: 85
pipeline_tag: question-answering
model_index:
  - name: AutoModel
    results:
      - task:
          type: vqa
          name: Visual Question Answering
        dataset:
          type: synthetdataset
          name: Synthetic Multimodal Dataset
          config: default
          split: test
          revision: main
        metrics:
          - type: accuracy
            value: 85
            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）。

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

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

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.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

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]
• 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 [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]