Instructions to use KamrusSamad/tiny2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KamrusSamad/tiny2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="KamrusSamad/tiny2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("KamrusSamad/tiny2") model = AutoModelForMaskedLM.from_pretrained("KamrusSamad/tiny2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from KamrusSamad/tiny2: direct link, hf CLI and curl.
- Browser
- Download file 516 Bytes
-
https://huggingface.co/KamrusSamad/tiny2/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://KamrusSamad/tiny2/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/KamrusSamad/tiny2/resolve/main/tokenizer_config.json
516 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": "ckiplab/albert-tiny-chinese", "special_tokens_map_file": "cache_dir/2d16250399060bdb8ae5dc2b0a58101797090778c606883b5928e4b9d00a2ed0.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "tokenizer_class": "BertTokenizer"} |