Sentence Similarity
sentence-transformers
PyTorch
Transformers
Chinese
bert
feature-extraction
semantic-search
chinese
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use DMetaSoul/sbert-chinese-general-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DMetaSoul/sbert-chinese-general-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DMetaSoul/sbert-chinese-general-v1") 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 DMetaSoul/sbert-chinese-general-v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DMetaSoul/sbert-chinese-general-v1") model = AutoModel.from_pretrained("DMetaSoul/sbert-chinese-general-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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revision: None
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metrics:
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- type: map_at_1
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value: 21
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- type: map_at_10
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value: 28.364
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- type: map_at_5
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value: 27.311999999999998
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- type: mrr_at_1
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value: 21
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- type: mrr_at_10
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value: 28.364
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- type: mrr_at_5
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value: 27.311999999999998
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- type: ndcg_at_1
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value: 21
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- type: ndcg_at_10
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value: 32.708
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- type: ndcg_at_5
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value: 30.23
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value: 21
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- type: precision_at_10
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value: 4.66
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- type: precision_at_5
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value: 7.82
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value: 21
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value: 46.6
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- type: recall_at_100
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value: 41.4
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value: 60
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- type: recall_at_3
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value: 20.5
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- type: recall_at_10
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value: 47.4
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value: 66
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value: 83
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value: 35
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value: 40.300000000000004
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- task:
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value: 65.11027167433211
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- type: f1
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value: 82.03549710974653
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---
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# DMetaSoul/sbert-chinese-general-v1
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revision: None
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metrics:
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- type: map_at_1
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value: 21
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| 428 |
- type: map_at_10
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| 429 |
value: 28.364
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| 430 |
- type: map_at_100
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- type: map_at_5
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value: 27.311999999999998
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- type: mrr_at_1
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value: 21
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- type: mrr_at_10
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value: 28.364
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- type: mrr_at_100
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- type: mrr_at_5
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value: 27.311999999999998
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- type: ndcg_at_1
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+
value: 21
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| 452 |
- type: ndcg_at_10
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value: 32.708
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- type: ndcg_at_100
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- type: ndcg_at_5
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value: 30.23
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- type: precision_at_1
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value: 21
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- type: precision_at_10
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value: 4.66
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- type: precision_at_100
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- type: precision_at_5
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value: 7.82
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- type: recall_at_1
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value: 21
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- type: recall_at_10
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value: 46.6
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- type: recall_at_100
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- type: recall_at_100
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value: 41.4
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- type: recall_at_1000
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value: 60
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- type: recall_at_3
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value: 20.5
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- type: recall_at_5
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- type: recall_at_10
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value: 47.4
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- type: recall_at_100
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value: 66
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- type: recall_at_1000
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value: 83
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- type: recall_at_3
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value: 35
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- type: recall_at_5
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value: 40.300000000000004
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- task:
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value: 65.11027167433211
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- type: f1
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value: 82.03549710974653
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license: apache-2.0
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language:
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- zh
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---
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# DMetaSoul/sbert-chinese-general-v1
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