Sentence Similarity
sentence-transformers
Safetensors
Transformers
Chinese
English
qwen2
feature-extraction
text-embeddings-inference
Instructions to use BAAI/bge-code-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-code-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-code-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 BAAI/bge-code-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-code-v1") model = AutoModel.from_pretrained("BAAI/bge-code-v1") - Notebooks
- Google Colab
- Kaggle
Slightly update Sentence Transformers snippet
#2
by tomaarsen HF Staff - opened
Hello!
Preface
I got it working now! Very nicely done, I quite like this parameter count for code embedding models. Are you planning a paper or blogpost with some more details? I'm curious about your training data setup.
Pull Request overview
- Use the trust_remote_code parameter immediately in the Sentence Transformers snippet
Details
You can use the trust_remote_code parameter immediately - it'll get propagated down to the model, tokenizer, and config when loading.
This simplifies the overall loading code somewhat.
- Tom Aarsen
tomaarsen changed pull request status to open
Thank you for your PR. We will present more details in our upcoming paper.
cfli changed pull request status to merged