Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Fantasim/logg-grouping-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Fantasim/logg-grouping-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Fantasim/logg-grouping-model") 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] - Transformers
How to use Fantasim/logg-grouping-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Fantasim/logg-grouping-model") model = AutoModel.from_pretrained("Fantasim/logg-grouping-model") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "do_lower_case": true, | |
| "eos_token": "</s>", | |
| "is_local": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 384, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "MPNetTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |