Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
semantic-search
chinese
text-embeddings-inference
Instructions to use DMetaSoul/sbert-chinese-dtm-domain-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DMetaSoul/sbert-chinese-dtm-domain-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DMetaSoul/sbert-chinese-dtm-domain-v1") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use DMetaSoul/sbert-chinese-dtm-domain-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DMetaSoul/sbert-chinese-dtm-domain-v1") model = AutoModel.from_pretrained("DMetaSoul/sbert-chinese-dtm-domain-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xiaowenbin commited on
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# Usage
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## 1. Sentence-Transformers
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- 定时25分钟 VS. 计时半个小时
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注:此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-dtm-domain-v1-distill),也已经开源啦!
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# Usage
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## 1. Sentence-Transformers
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