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