Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
roberta
feature-extraction
style
representation
text-embeddings-inference
Instructions to use TimKoornstra/SAURON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TimKoornstra/SAURON with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TimKoornstra/SAURON") 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 TimKoornstra/SAURON with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TimKoornstra/SAURON") model = AutoModel.from_pretrained("TimKoornstra/SAURON", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": false, | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "errors": "replace", | |
| "mask_token": "<mask>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "name_or_path": "/hpc/uu_cs_nlpsoc/tkoornstra/.cache/roberta-base", | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "special_tokens_map_file": null, | |
| "tokenizer_class": "RobertaTokenizer", | |
| "trim_offsets": true, | |
| "unk_token": "<unk>" | |
| } | |