Sentence Similarity
sentence-transformers
Safetensors
Transformers
Chinese
English
qwen2
feature-extraction
text-embeddings-inference
Instructions to use nebula2025/CodeR-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nebula2025/CodeR-full with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nebula2025/CodeR-full") 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 nebula2025/CodeR-full with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nebula2025/CodeR-full") model = AutoModel.from_pretrained("nebula2025/CodeR-full") - Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
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"AutoTokenizer": [
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