README.md

---
license: apache-2.0
language:
- zh
tags:
- text-classification
- chinese-nlp
- clips
- multi-modal
pipeline_tag: text-classification
library_name: transformers
---
# Model Card for Yougen/clips

<!-- Provide a quick summary of what the model is/does. -->

CLIPS (Contrastive Language-Image Pre-training) 中文多模态模型，基于CLIP架构在大规模中文图文数据集上进行预训练，能够实现文本与图像的跨模态匹配与检索。

## Model Details

### Model Description

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

本模型是CLIP架构的中文适配版本，通过对比学习的方式学习文本和图像的联合表示。模型能够将中文文本和图像映射到同一个特征空间，使得语义相似的文本和图像在特征空间中距离相近。该模型可用于图像检索、文本检索、零样本图像分类、图文匹配等多种多模态任务。

- **Developed by:** Yougen Yuan
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** Yougen Yuan
- **Model type:** Contrastive Language-Image Pre-training (CLIP)
- **Language(s) (NLP):** Chinese (zh)
- **License:** Apache-2.0
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://huggingface.co/Yougen/clips
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->

- 零样本图像分类：无需额外训练，直接使用中文文本描述对图像进行分类
- 图像检索：根据中文文本查询检索相关图像
- 文本检索：根据图像查询检索相关中文文本
- 图文相似度计算：计算任意中文文本与图像之间的语义相似度

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

- 多模态分类任务：在特定领域数据集上微调，实现更精准的图像分类
- 图像描述生成：结合生成式模型，基于图像生成中文描述
- 视觉问答(VQA)：结合问答模型，实现基于图像的中文问答
- 多模态检索系统：构建大规模图文检索引擎

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

- 不适用于非中文语言的多模态任务
- 不适用于需要高精度医学影像、卫星影像等专业领域的分析
- 禁止用于生成或传播违法、有害、歧视性内容
- 禁止用于未经授权的人脸识别或身份验证系统

## Bias, Risks, and Limitations

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

- 模型在预训练数据中学习到的社会偏见可能会在预测结果中体现
- 对于罕见物体、抽象概念和复杂场景的理解能力有限
- 模型性能受输入图像质量和文本描述准确性的影响较大
- 在低资源领域和长尾类别上的表现可能不佳
- 模型不具备因果推理能力，仅能学习数据中的统计相关性

### Recommendations

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

用户 (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 在使用本模型进行关键决策前，建议进行充分的测试和验证。对于高风险应用场景，应结合人工审核和其他验证手段。

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import CLIPProcessor, CLIPModel
import torch
from PIL import Image

# 加载模型和处理器
model = CLIPModel.from_pretrained("Yougen/clips")
processor = CLIPProcessor.from_pretrained("Yougen/clips")

# 准备输入
image = Image.open("example.jpg")
texts = ["一张猫的照片", "一张狗的照片", "一张鸟的照片"]

# 预处理
inputs = processor(text=texts, images=image, return_tensors="pt", padding=True)

# 前向传播
with torch.no_grad():
    outputs = model(**inputs)

# 计算相似度
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)

# 输出结果
for text, prob in zip(texts, probs[0]):
    print(f"{text}: {prob.item():.4f}")
```

## Training Details

### Training Data

<!-- This should link to a Dataset 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 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 [optional]

[More Information Needed]

#### Training Hyperparameters

- **Training regime:** fp16 mixed precision <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
- **Batch size:** [More Information Needed]
- **Learning rate:** [More Information Needed]
- **Epochs:** [More Information Needed]
- **Optimizer:** AdamW

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

<!-- 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 Dataset 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. -->

- **Top-k 准确率:** 用于评估零样本图像分类性能
- **Recall@k:** 用于评估图像检索和文本检索性能
- **mAP (mean Average Precision):** 用于评估检索系统的整体性能

### Results

[More Information Needed]

#### Summary

[More Information Needed]

## Model Examination [optional]

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

[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

本模型采用CLIP架构，包含两个主要组件：
- **图像编码器:** 基于Vision Transformer (ViT) 架构，将图像转换为固定维度的特征向量
- **文本编码器:** 基于Transformer架构，将中文文本转换为固定维度的特征向量

模型通过对比学习损失函数进行训练，最大化匹配的图文对之间的相似度，最小化不匹配的图文对之间的相似度。

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

- **Framework:** PyTorch
- **Libraries:** transformers, datasets, torchvision
- **Training Platform:** [More Information Needed]

## 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:**

```bibtex
@misc{yuan2026clips,
  author = {Yougen Yuan},
  title = {CLIPS: Chinese Contrastive Language-Image Pre-training Model},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Yougen/clips}}
}
```

**APA:**

Yuan, Y. (2026). CLIPS: Chinese Contrastive Language-Image Pre-training Model. Hugging Face. https://huggingface.co/Yougen/clips

## Glossary [optional]

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

- **CLIP:** Contrastive Language-Image Pre-training，一种通过对比学习实现跨模态表示学习的方法
- **零样本学习:** 无需在特定任务数据集上进行训练，直接使用预训练模型完成任务
- **跨模态检索:** 在不同模态（如文本和图像）之间进行信息检索

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

Yougen Yuan

## Model Card Contact

[More Information Needed]