Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
rag
universal-embedding
matryoshka
embeddings
information-retrieval
Eval Results (legacy)
text-embeddings-inference
Instructions to use IstishadAlamTishad/TensorFluxEmbedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use IstishadAlamTishad/TensorFluxEmbedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("IstishadAlamTishad/TensorFluxEmbedder") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 380 Bytes
93f6723 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"backend": "tokenizers",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"is_local": false,
"local_files_only": false,
"mask_token": "[MASK]",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 8192,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"tokenizer_class": "TokenizersBackend",
"unk_token": "[UNK]"
}
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