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
| { | |
| "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" | |
| } | |
| } |