Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use Gpaiva/NERDE-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gpaiva/NERDE-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Gpaiva/NERDE-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Gpaiva/NERDE-base") model = AutoModelForTokenClassification.from_pretrained("Gpaiva/NERDE-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Gpaiva/NERDE-base: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/Gpaiva/NERDE-base/resolve/02a72c8ac71447c1886388680e1d4c7801f830dd/training_args.bin
- Command line
-
hf download hf://Gpaiva/NERDE-base@02a72c8ac71447c1886388680e1d4c7801f830dd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Gpaiva/NERDE-base/resolve/02a72c8ac71447c1886388680e1d4c7801f830dd/training_args.bin
3.25 kB
- Xet hash:
- d3ddffde4d9a941dcc026e8ae2d78519dab563a8ff24cb402e85f26a44164d5a
- Size of remote file:
- 3.25 kB
- SHA256:
- 7229bc58879699fe61b64557d83a5d7f0aabd8ec32235be567347e444da5ad82
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.