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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
SentenceModel(model_name_or_path = 'text2vec-base-multilingual')时报错
#2
by yangrong20230920 - opened
TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType
按readme的使用说明来,model = SentenceModel('shibing624/text2vec-base-multilingual')
可以的,我把推理脚本搞错了,谢谢
shibing624 changed discussion status to closed