Instructions to use BSC-LT/NextProcurement_pdfutils with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-LT/NextProcurement_pdfutils with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BSC-LT/NextProcurement_pdfutils")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BSC-LT/NextProcurement_pdfutils") model = AutoModelForSequenceClassification.from_pretrained("BSC-LT/NextProcurement_pdfutils", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from BSC-LT/NextProcurement_pdfutils: direct link, hf CLI and curl.
- Browser
- Download file 340 Bytes
-
https://huggingface.co/BSC-LT/NextProcurement_pdfutils/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://BSC-LT/NextProcurement_pdfutils/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/BSC-LT/NextProcurement_pdfutils/resolve/main/tokenizer_config.json
340 Bytes
| { | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "mask_token": "[MASK]", | |
| "name_or_path": "models/bert-base-multilingual-cased", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": null, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |