Create README.md

README.md

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+## CLIP-Vit-Bert-Chinese pretrained model
+这是中文版本的CLIP预训练模型，基于LiT-tuning（Locked-image Text tuning）的策略，使用140万中文图文对数据进行多模态对比学习预训练。
+
+Github: [CLIP-Chinese](https://github.com/yangjianxin1/CLIP-Chinese)
+
+Bolg: [CLIP-Chinese：中文多模态对比学习CLIP预训练模型]()
+
+## Model and Training Detail
+该模型主要由文本编码器与图像编码器组成，其中文本编码器为Bert，图像编码器为Vit，我们将该模型称为BertCLIP模型。训练时，Bert使用Langboat/mengzi-bert-base的权重进行初始化，Vit使用openai/clip-vit-large-patch32
+的权重进行初始化。采用LiT-tuning（Locked-image Text tuning）的策略进行训练，也就是冻结Vit的权重，训练BertCLIP模型剩余的权重。
+
+## Usage
+首先将项目clone到本地，并且安装依赖包
+```bash
+git clone https://github.com/yangjianxin1/CLIP-Chinese
+pip install -r requirements.txt
+```
+
+使用如下脚本，就可成功加载预训练权重，对图片和文本进行预处理，并且得到模型的输出
+```python
+from transformers import CLIPProcessor
+from component.model import BertCLIPModel
+from PIL import Image
+import requests
+
+model_name_or_path = 'YeungNLP/clip-vit-bert-chinese-1M'
+# 加载预训练模型权重
+model = BertCLIPModel.from_pretrained(model_name_or_path)
+CLIPProcessor.tokenizer_class = 'BertTokenizerFast'
+# 初始化processor
+processor = CLIPProcessor.from_pretrained(model_name_or_path)
+# 预处理输入
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = processor(text=["一只小狗在摇尾巴", "一只小猪在吃饭"], images=image, return_tensors="pt", padding=True)
+inputs.pop('token_type_ids')    # 输入中不包含token_type_ids
+
+outputs = model(**inputs)
+
+# 对于每张图片，计算其与所有文本的相似度
+logits_per_image = outputs.logits_per_image  # image-text的相似度得分
+probs = logits_per_image.softmax(dim=1)  # 对分数进行归一化
+
+# 对于每个文本，计算其与所有图片的相似度
+logits_per_text = outputs.logits_per_text  # text-image的相似度得分
+probs = logits_per_text.softmax(dim=1)  # 对分数进行归一化
+
+# 获得文本编码
+text_embeds = outputs.text_embeds
+# 获得图像编码
+image_embeds = outputs.image_embeds
+```
+
+单独加载图像编码器，进行下游任务
+```python
+from PIL import Image
+import requests
+from transformers import CLIPProcessor, CLIPVisionModel
+
+model_name_or_path = 'YeungNLP/clip-vit-bert-chinese-1M'
+model = CLIPVisionModel.from_pretrained(model_name_or_path)
+CLIPProcessor.tokenizer_class = 'BertTokenizerFast'
+processor = CLIPProcessor.from_pretrained(model_name_or_path)
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = processor(images=image, return_tensors="pt")
+
+outputs = model(**inputs)
+last_hidden_state = outputs.last_hidden_state
+pooled_output = outputs.pooler_output 
+```
+
+单独加载文本编码器，进行下游任务
+
+```python
+from component.model import BertCLIPTextModel
+from transformers import BertTokenizerFast
+
+model_name_or_path = 'YeungNLP/clip-vit-bert-chinese-1M'
+model = BertCLIPTextModel.from_pretrained(model_name_or_path)
+tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
+
+inputs = tokenizer(["一只小狗在摇尾巴", "一只小猪在吃饭"], padding=True, return_tensors="pt")
+inputs.pop('token_type_ids')  # 输入中不包含token_type_ids
+
+outputs = model(**inputs)
+last_hidden_state = outputs.last_hidden_state
+pooled_output = outputs.pooler_output
+```