Sentence Similarity
sentence-transformers
PyTorch
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Gflorent/fossil_flex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Gflorent/fossil_flex with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Gflorent/fossil_flex") 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
Download tokenizer_config.json from Gflorent/fossil_flex: direct link, hf CLI and curl.
- Browser
- Download file 447 Bytes
-
https://huggingface.co/Gflorent/fossil_flex/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Gflorent/fossil_flex/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Gflorent/fossil_flex/resolve/main/tokenizer_config.json
447 Bytes
| {"do_lower_case": true, "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "[UNK]", "pad_token": "<pad>", "mask_token": "<mask>", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "/home/florent_huggingface_co/.cache/torch/sentence_transformers/sentence-transformers_all-mpnet-base-v2/", "tokenizer_class": "MPNetTokenizer"} |