Sentence Similarity
Transformers
Safetensors
sentence-transformers
Chinese
utu
feature-extraction
text-embeddings-inference
custom_code
Instructions to use DIYIN/Youtu-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DIYIN/Youtu-Embedding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DIYIN/Youtu-Embedding", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use DIYIN/Youtu-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DIYIN/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
Download 1_Pooling/config.json from DIYIN/Youtu-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 314 Bytes
-
https://huggingface.co/DIYIN/Youtu-Embedding/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://DIYIN/Youtu-Embedding/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/DIYIN/Youtu-Embedding/resolve/main/1_Pooling/config.json
314 Bytes
| { | |
| "word_embedding_dimension": 2048, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": false | |
| } |