Instructions to use SamuelYang/SentMAE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SamuelYang/SentMAE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SamuelYang/SentMAE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SamuelYang/SentMAE") model = AutoModelForMaskedLM.from_pretrained("SamuelYang/SentMAE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from SamuelYang/SentMAE: direct link, hf CLI and curl.
- Browser
- Download file 553 Bytes
-
https://huggingface.co/SamuelYang/SentMAE/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://SamuelYang/SentMAE/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/SamuelYang/SentMAE/resolve/main/tokenizer_config.json
553 Bytes
| {"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, "model_max_length": 512, "name_or_path": "/ads-nfs/t-shxiao/pretrain_retriever/saved_models/alltokensv2_layer1_encoder015_decoder05/", "special_tokens_map_file": "/ads-nfs/t-shxiao/pretrain_retriever/saved_models/alltokensv2_layer1_encoder015_decoder05/special_tokens_map.json", "tokenizer_class": "BertTokenizer"} |