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
| { | |
| "mlflow_metadata": { | |
| "run_info": {}, | |
| "metrics": {}, | |
| "params": {}, | |
| "tags": {} | |
| }, | |
| "user_data": { | |
| "task_type": "ner", | |
| "language": "en", | |
| "dataset": "ner_distillbert", | |
| "description": "This is a Named Entity Recognition (NER) model based on DistilBERT, a distilled version of BERT that retains 97% of BERT's performance while being 60% smaller and faster. The model identifies and classifies named entities in text such as persons, organizations, locations, and other predefined categories.", | |
| "author": "BITZ-AI TEAM", | |
| "license": "apache-2.0" | |
| }, | |
| "export_info": { | |
| "framework": "pytorch", | |
| "task_type": "ner", | |
| "export_timestamp": "2025-09-29T13:24:15.668519", | |
| "version": "1" | |
| } | |
| } |