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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8edad25d4d18f378482e294f69d5c27ccaa962ce4ff6dd70c13879fa0f77480f
- Size of remote file:
- 11.4 MB
- SHA256:
- a56524092f5d0676e63537511b535e73e7580a7efe440247ef3fa43d019a0af0
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