Sentence Similarity
sentence-transformers
Safetensors
multilingual
neomme
multimodal
document-retrieval
dense-retrieval
Instructions to use Hcompany/NeoMME-800M-Retriever-ST-dense with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hcompany/NeoMME-800M-Retriever-ST-dense with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hcompany/NeoMME-800M-Retriever-ST-dense") 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: 515 Bytes
b043d83 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"backend": "tokenizers",
"clean_up_tokenization_spaces": false,
"document_token": "<doc>",
"eos_token": "<eos>",
"extra_special_tokens": [],
"image_token": "<img>",
"mask_token": "<mask>",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 16384,
"pad_token": "<pad>",
"processor_class": "NeoMMEProcessor",
"query_token": "<query>",
"row_token": "<row>",
"tokenizer_class": "TokenizersBackend",
"truncation_side": "right",
"unk_token": "<unk>"
}
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