sentence-transformers
ONNX
Safetensors
English
ogma
mteb
embedding
text-embedding
axiotic
matryoshka
small-model
custom_code
Eval Results (legacy)
Instructions to use axiotic/ogma-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use axiotic/ogma-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("axiotic/ogma-large", trust_remote_code=True) 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
| { | |
| "tokenizer_class": "OgmaTokenizerFast", | |
| "auto_map": { | |
| "AutoTokenizer": [ | |
| null, | |
| "tokenization_ogma.OgmaTokenizerFast" | |
| ] | |
| }, | |
| "model_max_length": 1024, | |
| "padding_side": "right", | |
| "pad_token": "<pad>", | |
| "unk_token": "<unk>", | |
| "cls_token": "[CLS]", | |
| "sep_token": "[SEP]", | |
| "bos_token": "[CLS]", | |
| "eos_token": "[SEP]", | |
| "mask_token": "[MASK]", | |
| "do_lower_case": true, | |
| "backend": "tokenizers" | |
| } | |