Sentence Similarity
sentence-transformers
Safetensors
Greek
English
ministral3
greek
english
retrieval
rag
embeddings
nemotron
Eval Results (legacy)
Instructions to use KIEFERSA/Sophea-Nemo-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KIEFERSA/Sophea-Nemo-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KIEFERSA/Sophea-Nemo-Embedding") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 1,093 Bytes
e5b0973 | 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 38 39 40 41 42 43 44 45 46 47 | {
"architectures": [
"Ministral3Model"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 6144,
"is_causal": false,
"llama_4_scaling": {
"beta": 0.1,
"original_max_position_embeddings": 16384
},
"max_position_embeddings": 262144,
"model_type": "ministral3",
"nemo_version": "0.3.0rc0",
"num_attention_heads": 24,
"num_hidden_layers": 16,
"num_key_value_heads": 8,
"pad_token_id": 11,
"pooling": "avg",
"rms_norm_eps": 1e-05,
"rope_parameters": {
"apply_yarn_scaling": false,
"beta_fast": 32.0,
"beta_slow": 1.0,
"factor": 16.0,
"llama_4_scaling_beta": 0.1,
"mscale": 1.0,
"mscale_all_dim": 1.0,
"original_max_position_embeddings": 16384,
"rope_theta": 1000000.0,
"rope_type": "yarn",
"type": "yarn"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.13.0",
"use_cache": false,
"vocab_size": 131072
}
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