Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Chinese
bert
Sentence Transformers
Instructions to use shibing624/text2vec-base-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shibing624/text2vec-base-chinese with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shibing624/text2vec-base-chinese") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Modify a small descriptive mistake in the usage example
#2
by Haxy - opened
README.md
CHANGED
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@@ -75,7 +75,7 @@ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tenso
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case,
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case, mean pooling.
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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