Sentence Similarity
Transformers
Safetensors
sentence-transformers
Chinese
utu
feature-extraction
text-embeddings-inference
custom_code
Instructions to use tencent/Youtu-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/Youtu-Embedding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tencent/Youtu-Embedding", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use tencent/Youtu-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tencent/Youtu-Embedding", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
springxchen commited on
Update config_sentence_transformers.json
Browse files
config_sentence_transformers.json
CHANGED
|
@@ -6,7 +6,7 @@
|
|
| 6 |
"pytorch": "2.5.1+cu118"
|
| 7 |
},
|
| 8 |
"prompts": {
|
| 9 |
-
"query": "Instruction: Given a
|
| 10 |
"document": "",
|
| 11 |
"passage": ""
|
| 12 |
},
|
|
|
|
| 6 |
"pytorch": "2.5.1+cu118"
|
| 7 |
},
|
| 8 |
"prompts": {
|
| 9 |
+
"query": "Instruction: Given a search query, retrieve passages that answer the question \nQuery:",
|
| 10 |
"document": "",
|
| 11 |
"passage": ""
|
| 12 |
},
|