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
- Xet hash:
- d3ddffde4d9a941dcc026e8ae2d78519dab563a8ff24cb402e85f26a44164d5a
- Size of remote file:
- 3.25 kB
- SHA256:
- 7229bc58879699fe61b64557d83a5d7f0aabd8ec32235be567347e444da5ad82
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