Instructions to use pdelobelle/robbert-v2-dutch-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pdelobelle/robbert-v2-dutch-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pdelobelle/robbert-v2-dutch-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pdelobelle/robbert-v2-dutch-ner") model = AutoModelForTokenClassification.from_pretrained("pdelobelle/robbert-v2-dutch-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pdelobelle/robbert-v2-dutch-ner: direct link, hf CLI and curl.
- Browser
- Download file 465 MB
-
https://huggingface.co/pdelobelle/robbert-v2-dutch-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pdelobelle/robbert-v2-dutch-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pdelobelle/robbert-v2-dutch-ner/resolve/main/pytorch_model.bin
465 MB
- Xet hash:
- f10f7e1685dead12c5ae8f81fde2dbfe3872ba9fd5a75917d3214e9900ff59c7
- Size of remote file:
- 465 MB
- SHA256:
- 706516e446ef39b4c824d206377d4f03c48ce321066a4841ff74fb37d30104db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.