CLIP-ViT-B-32-multilingual-v1 是一个用于多语言图像理解任务的模型,它使用 ViT(Vision Transformer)架构,并针对多语言环境进行了优化。这个模型可以处理多种语言的图像数据,并且能够理解和生成与图像相关的文本信息。通过使用多语言版本的 CLIP 数据集进行训练,该模型能够更好地适应不同语言和文化背景下的图像理解...
transformer_model=SentenceTransformer('clip-ViT-B-32-multilingual-v1',cache_folder='model_pytorch')tokenizer=transformer_model.tokenizer### this is from dense model configurationdense_model=models.Dense(in_features=768,out_features=512,bias=False,activation_function=nn.Identity() )### load the wei...
镜像cr.weaviate.io/semitechnologies/multi2vec-clip:sentence-transformers-clip-ViT-B-32-multilingual-v1 同步失败详情请查看如有疑问请回复 /auto-cc 召唤帮助, 或者查看同步成功案例或白名单申请案例 github-actionsadded sync image failure on Jan 7, 2025 github-actionsclosed this as not plannedon Jan...
用于多语言图像理解的 CLIP-ViT-B-32-multilingual-v1 模型,用于多语言环境下的图像理解任务。 (0)踩踩(0) 所需:1积分 Option_Trend 2025-04-02 00:00:16 积分:1 stock-ai-pc 2025-04-02 00:00:54 积分:1 DSPCourseDesign 2025-04-02 00:07:08 ...
I was working on exporting sentence-transformers/clip-ViT-B-32-multilingual-v1 model to onnx without success. Finally I figured out by manipulating model with torch.onnx. I can contribute onnx model to fastembed-rs with the custom steps of torch.onnx export(python script). ...