Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
bert
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use BAAI/bge-base-en-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-base-en-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-base-en-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-base-en-v1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-base-en-v1.5")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-base-en-v1.5") model = AutoModel.from_pretrained("BAAI/bge-base-en-v1.5", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca9373f01cdedabfa2ee4adafdb468ff0f41145959511f0400d807bd98caedea
- Size of remote file:
- 438 MB
- SHA256:
- 4b9d4a4b2d7ab06e3861861d5ee370974b31d17c0bb6af6f13d3dc79f33bbdcd
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