Feature Extraction
sentence-transformers
PyTorch
Transformers
Chinese
bert
sentence-similarity
text-embeddings-inference
Instructions to use BAAI/bge-large-zh-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-large-zh-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-large-zh-v1.5") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use BAAI/bge-large-zh-v1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-large-zh-v1.5")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-large-zh-v1.5") model = AutoModel.from_pretrained("BAAI/bge-large-zh-v1.5", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
update
Browse files
README.md
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More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
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[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
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FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
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More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
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[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
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FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
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