Instructions to use hanhainebula/baai-multilingual-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hanhainebula/baai-multilingual-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hanhainebula/baai-multilingual-large") 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 hanhainebula/baai-multilingual-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hanhainebula/baai-multilingual-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hanhainebula/baai-multilingual-large") model = AutoModel.from_pretrained("hanhainebula/baai-multilingual-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fde8653f2f656fb4ab30c2a5db64ba86a916a86134355b76c3bf26a5b022b323
- Size of remote file:
- 4.24 MB
- SHA256:
- 61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
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