Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Chinese
ernie
feature-extraction
text2vec
Instructions to use shibing624/text2vec-base-chinese-paraphrase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shibing624/text2vec-base-chinese-paraphrase with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shibing624/text2vec-base-chinese-paraphrase") 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-chinese-paraphrase with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("shibing624/text2vec-base-chinese-paraphrase") model = AutoModel.from_pretrained("shibing624/text2vec-base-chinese-paraphrase", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 690c24484e1e7c5996e458f7e9794fbbb96a60793dde1ebebb1f21b7ccf4435d
- Size of remote file:
- 472 MB
- SHA256:
- 2a72718cd5a79d366003be1dbd1761e4ecc575cc5075b0d387ea0f09eecc9d1f
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