Sentence Similarity
Transformers
Safetensors
Ganda
sagepond_un_model
feature-extraction
luganda
embeddings
sentence-embeddings
semantic-search
research
custom_code
Eval Results (legacy)
Instructions to use sagepond/embedding-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sagepond/embedding-small with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sagepond/embedding-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 650 Bytes
8e1984a | 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 | {
"architectures": [
"UnModel"
],
"auto_map": {
"AutoConfig": "ganda_config.UnModelConfig",
"AutoModel": "ganda_model.UnModel",
"AutoTokenizer": [
"tokenization_un_model.UnModelTokenizer",
null
]
},
"bos_token_id": 2,
"dropout": 0.1,
"dtype": "float32",
"embedding_dim": 3072,
"eos_token_id": 3,
"hidden_act": "relu",
"hidden_size": 768,
"intermediate_size": 3072,
"max_position_embeddings": 600,
"model_type": "sagepond_un_model",
"num_attention_heads": 16,
"num_hidden_layers": 12,
"pad_token_id": 0,
"transformers_version": "5.20.2",
"unk_token_id": 1,
"vocab_size": 40000
}
|