Feature Extraction
sentence-transformers
PyTorch
Transformers
English
Chinese
xlm-roberta
sentence-similarity
text-embeddings-inference
Instructions to use maidalun1020/bce-embedding-base_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use maidalun1020/bce-embedding-base_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("maidalun1020/bce-embedding-base_v1") 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 maidalun1020/bce-embedding-base_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="maidalun1020/bce-embedding-base_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("maidalun1020/bce-embedding-base_v1") model = AutoModel.from_pretrained("maidalun1020/bce-embedding-base_v1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
请问支持的文本长度是多少
#9
by shaunxu - opened
我看例子里面写的max_length=512,是否支持512 token?即此后的内容在计算向量的时候会被忽略。
yes
最长 token支持是多少?
512 tokens
maidalun1020 changed discussion status to closed