Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use kaiserrr/pmc-vit-l-14-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kaiserrr/pmc-vit-l-14-multilingual with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kaiserrr/pmc-vit-l-14-multilingual") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use kaiserrr/pmc-vit-l-14-multilingual with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kaiserrr/pmc-vit-l-14-multilingual") model = AutoModel.from_pretrained("kaiserrr/pmc-vit-l-14-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b13b40052d5fa9fc73540a5a373e97fb405fc2131b0c7b3bd77b2fed1a3ac20e
- Size of remote file:
- 1.11 GB
- SHA256:
- 0dbada443557e703e0f5fc9a4fbba66f3f67d385905ff5c038c20a0bda0508a1
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