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
File size: 432 Bytes
07205b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"label_to_id": {
"PERPETRATOR": 0,
"LOCATION": 1,
"LANDMARK": 2,
"PHONE_NUMBER": 3,
"GENDER": 4,
"AGE": 5,
"O": 6,
"INCIDENT_TYPE": 7,
"NAME": 8,
"VICTIM": 9
},
"id_to_label": {
"0": "PERPETRATOR",
"1": "LOCATION",
"2": "LANDMARK",
"3": "PHONE_NUMBER",
"4": "GENDER",
"5": "AGE",
"6": "O",
"7": "INCIDENT_TYPE",
"8": "NAME",
"9": "VICTIM"
}
} |