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
File size: 974 Bytes
79dac1d 8eb18f9 79dac1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | {
"architectures": [
"UTUModel"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_youtu.UTUConfig",
"AutoModel": "modeling_youtu.UTUModel",
"AutoModelForCausalLM": "modeling_youtu.UTUForCausalLM"
},
"bos_token_id": 128000,
"embedding_initializer_range": 0.02795084971874737,
"eos_token_id": 128001,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.013975424859373685,
"intermediate_size": 8192,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "utu",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"pretraining_tp": 1,
"rms_norm_eps": 1e-08,
"rope_scaling": null,
"rope_theta": 1600000.0,
"tie_word_embeddings": true,
"tokenizer_class": "PreTrainedTokenizerFast",
"torch_dtype": "float32",
"transformers_version": "4.51.3",
"use_cache": false,
"vocab_size": 128256
}
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