Token Classification
Scikit-learn
PyTorch
TensorBoard
Safetensors
Transformers
English
distilbert
ner
mlflow
openchs
Eval Results (legacy)
Instructions to use openchs/ner_distillbert_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use openchs/ner_distillbert_v1 with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("openchs/ner_distillbert_v1", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Transformers
How to use openchs/ner_distillbert_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="openchs/ner_distillbert_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("openchs/ner_distillbert_v1") model = AutoModelForTokenClassification.from_pretrained("openchs/ner_distillbert_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5595fda75a70e6e94930bab785be888de3ac7eb9b526de6d8b63e50357d4b98
- Size of remote file:
- 261 MB
- SHA256:
- 215db0321a1a85118a83e7da4dac593eb20c073d8bc4a71d24a2b31f68ee3e34
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