Instructions to use DeepaPeri/MBERT-TRAINED-CONLL2002-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepaPeri/MBERT-TRAINED-CONLL2002-nl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="DeepaPeri/MBERT-TRAINED-CONLL2002-nl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DeepaPeri/MBERT-TRAINED-CONLL2002-nl") model = AutoModelForTokenClassification.from_pretrained("DeepaPeri/MBERT-TRAINED-CONLL2002-nl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from DeepaPeri/MBERT-TRAINED-CONLL2002-nl: direct link, hf CLI and curl.
- Browser
- Download file 40 Bytes
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https://huggingface.co/DeepaPeri/MBERT-TRAINED-CONLL2002-nl/resolve/main/tokenizer_config.json
- Command line
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hf download hf://DeepaPeri/MBERT-TRAINED-CONLL2002-nl/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/DeepaPeri/MBERT-TRAINED-CONLL2002-nl/resolve/main/tokenizer_config.json
40 Bytes
| {"do_lower_case": false, "max_len": 512} |