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clip-ViT-B-32-text
Qdrant/clip-ViT-B-32-text
clip-ViT-B-32-text is a sentence similarity model from Qdrant. Use it when you need a score for how close two texts are. It is set up for transformers. The card lists the license as mit.
ONNX port of sentence-transformers/clip-ViT-B-32 for text classification and similarity searches.
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Repo size
254 MB
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3
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At a glance
- Task
- Sentence Similarity
- Library
- transformers
- License
- mit
- Model type
- clip_text_model
- Access
- Public
- Created
- Apr 30, 2024
- Updated
- Oct 16, 2024
- SHA
- 48ca1db2
Model card
From the Hugging Face model README
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)
# ]
- Task
- Sentence Similarity
- Library
- transformers
- Type
- clip_text_model
- License
- mit
- Created
- Apr 30, 2024
- Updated
- Oct 16, 2024