Instructions to use uer/sbert-base-chinese-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use uer/sbert-base-chinese-nli with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("uer/sbert-base-chinese-nli") sentences = [ "那个人很开心", "那个人非常开心", "那只猫很开心", "那个人在吃东西" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use uer/sbert-base-chinese-nli with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("uer/sbert-base-chinese-nli") model = AutoModel.from_pretrained("uer/sbert-base-chinese-nli") - Inference
- Notebooks
- Google Colab
- Kaggle
| {"do_lower_case": true, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "sbert"} |