Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use hunkim/sentence-transformersklue-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hunkim/sentence-transformersklue-bert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hunkim/sentence-transformersklue-bert-base") 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 hunkim/sentence-transformersklue-bert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hunkim/sentence-transformersklue-bert-base") model = AutoModel.from_pretrained("hunkim/sentence-transformersklue-bert-base", device_map="auto") - 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, "do_basic_tokenize": true, "never_split": null, "model_max_length": 512, "special_tokens_map_file": "/home/hunkim/.cache/huggingface/transformers/aeaaa3afd086a040be912f92ffe7b5f85008b744624f4517c4216bcc32b51cf0.054ece8d16bd524c8a00f0e8a976c00d5de22a755ffb79e353ee2954d9289e26", "name_or_path": "model_output/training_klue_sts_klue-bert-base/", "tokenizer_class": "BertTokenizer"} |