Instructions to use Scezui/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scezui/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Scezui/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Scezui/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("Scezui/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Scezui/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 480 Bytes
-
https://huggingface.co/Scezui/layoutlm-funsd/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Scezui/layoutlm-funsd/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Scezui/layoutlm-funsd/resolve/main/tokenizer_config.json
480 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, "max_len": 512, "model_max_length": 512, "special_tokens_map_file": "/root/.cache/huggingface/transformers/48c3f426580c1b3278dbebb8c8dd372ea1549792f092b4f6fae1e21881c2cbd9.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "name_or_path": "microsoft/layoutlm-base-uncased"} |