Instructions to use kornwtp/ConGen-BERT-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kornwtp/ConGen-BERT-Mini with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kornwtp/ConGen-BERT-Mini") 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 kornwtp/ConGen-BERT-Mini with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kornwtp/ConGen-BERT-Mini") model = AutoModel.from_pretrained("kornwtp/ConGen-BERT-Mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from kornwtp/ConGen-BERT-Mini: direct link, hf CLI and curl.
- Browser
- Download file 549 Bytes
-
https://huggingface.co/kornwtp/ConGen-BERT-Mini/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://kornwtp/ConGen-BERT-Mini/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/kornwtp/ConGen-BERT-Mini/resolve/main/tokenizer_config.json
549 Bytes
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": "/root/.cache/huggingface/transformers/4a0e97e62ce20b646bfbff1b02fda670db76e7ea320160c4408162a9c1db89d6.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "name_or_path": "nreimers/BERT-Mini_L-4_H-256_A-4", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"} |