Qdrant/clip-ViT-B-32-text

🤗 Hugging Face 来源sentence-similaritymit254 MBother✓ 1 个校验和今天更新

一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对，然后提交。你只需开始做种，并粘贴你账户中的密钥。它只读取你的文件，绝不修改。如果愿意，可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo Qdrant/clip-ViT-B-32-text ./model-folder

需要做种者 →

ONNX port of sentence-transformers/clip-ViT-B-32 for text classification and similarity searches.

Usage

Here's an example of performing inference using the model with FastEmbed.

from fastembed import TextEmbedding

documents = [
    "You should stay, study and sprint.",
    "History can only prepare us to be surprised yet again.",
]

model = TextEmbedding(model_name="Qdrant/clip-ViT-B-32-text")
embeddings = list(model.embed(documents))

# [
#     array([1.57889184e-02, -2.21896712e-02, -1.40235685e-02, -2.36918423e-02, ...],
#           dtype=float32)
# ]