Sentence Similarity
sentence-transformers
PyTorch
distilbert
feature-extraction
text-embeddings-inference
Instructions to use ronanki/ml_use_13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ronanki/ml_use_13 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ronanki/ml_use_13") 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
| {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "max_len": 512, "special_tokens_map_file": "/home/reimers/.cache/torch/sentence_transformers/sbert.net_models_distiluse-base-multilingual-cased/0_DistilBERT/special_tokens_map.json", "full_tokenizer_file": null, "name_or_path": "/Users/avinashronanki/models/ml_use_13/", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "DistilBertTokenizer"} |