Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
bert
feature-extraction
text2vec
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use shibing624/text2vec-base-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shibing624/text2vec-base-multilingual with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shibing624/text2vec-base-multilingual") sentences = [ "那是 個快樂的人", "那是 條快樂的狗", "那是 個非常幸福的人", "今天是晴天" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use shibing624/text2vec-base-multilingual with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("shibing624/text2vec-base-multilingual") model = AutoModel.from_pretrained("shibing624/text2vec-base-multilingual") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54cc59535c0271afac7d7bccbead50afb6c8045a16f1ddee9c122375dedaad0f
- Size of remote file:
- 17.1 MB
- SHA256:
- b93bf61272f75c0a0b96b85fa262d2242e8a46008d76095386e98675f0bdd119
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