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
| { | |
| "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 | |
| } | |