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: 739 Bytes
215017d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"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"
}
} |