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
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c9b9d7210f64e6e7d207b39244dfddb7a9af8dc2c70c360c5836224c8334b241
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size 470641600
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