Feature Extraction
sentence-transformers
Russian
xlm-roberta
quantization
int8
text-embeddings-inference
Instructions to use AtesiT/bge-m3-int8-dynamic-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AtesiT/bge-m3-int8-dynamic-quantized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AtesiT/bge-m3-int8-dynamic-quantized") 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: 409 Bytes
b16b19c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"add_prefix_space": true,
"backend": "tokenizers",
"bos_token": "<s>",
"clean_up_tokenization_spaces": true,
"cls_token": "<s>",
"eos_token": "</s>",
"is_local": false,
"local_files_only": false,
"mask_token": "<mask>",
"model_max_length": 8192,
"pad_token": "<pad>",
"sep_token": "</s>",
"sp_model_kwargs": {},
"tokenizer_class": "XLMRobertaTokenizer",
"unk_token": "<unk>"
}
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