Instructions to use sa1tyeggs/text2vec-base-chinese_128DIM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sa1tyeggs/text2vec-base-chinese_128DIM with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sa1tyeggs/text2vec-base-chinese_128DIM") 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] - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- 2_Dense/model.safetensors +3 -0
- model.safetensors +3 -0
2_Dense/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:253382f93a0487b70a571c558a41e17d5c9353b85274d18b7340366e4c419e4d
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f7afb28efb04963d5b478282940cb291d88a5a604c79277b0eacb93dfb5f4606
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size 409097104
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