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openchs/ner_distillbert_v1

sourceHugging Faceapache-2.0updated 11mo agoView on Hugging Face

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Model Card

ner-distilbert-base-cased

This is a Named Entity Recognition (NER) model based on the distilbert-base-cased architecture. This model was expertly fine-tuned to serve a critical function: accurately identifying and classifying key information within the specific domain of child helpline conversations. Through its specialized training, the model has been optimized to excel at recognizing the following entities crucial for this context:

CALLER: The person initiating the call for help. COUNSELOR: The helpline staff member providing assistance. VICTIM: The person who is the subject of the issue being discussed. PERPETRATOR: The person causing the harm or issue. LOCATION: Geographic places relevant to the conversation. AGE: The age of the individuals involved. GENDER: Gender references. INCIDENT_TYPE: The specific type of problem or issue being reported (e.g., "bullying", "abuse" ).

Model Details

—Model Name: ner-distilbert-base-cased
—Version: 1
—Task: Ner
—Languages: en
—Framework: sklearn
—License: apache-2.0

Intended Uses & Limitations

Scope of Use

In Scope: The model is intended for text that is structurally and contextually similar to the training data (i.e., conversational, first-person accounts of issues). Out of Scope: This model is not intended for general-purpose NER tasks (e.g., analyzing news articles, legal documents, or emails). Its performance on text outside of its specialized domain has not been evaluated.

Training Data

—Dataset: nerdataset1.jsonl
—Size: Not specified
—Languages: en
—Nature: The training data is synthetic, generated to mimic real-world conversations while protecting the privacy and confidentiality of actual helpline users.

Training Configuration

Parameter	Value
Author	Rogendo
Batch Size	4
Epochs	10
Lr	2e-05
Model Name	distilbert-base-cased
Test Size	0.1
Training Date	2025-10-30T11:58:48.315647
Weight Decay	0.01

Performance Metrics

Evaluation Results

Metric	Value
Epoch	10.0000
Eval Accuracy	0.9930
Eval F1	0.9929
Eval Loss	0.0216
Eval Precision	0.9933
Eval Recall	0.9930
Eval Runtime	0.1509
Eval Samples Per Second	106.0170
Eval Steps Per Second	13.2520

Usage

Installation

bash

pip install transformers torch

Named Entity Recognition Example

python

from transformers import pipeline

Use the model from the openchs repository on Hugging Face
  ner = pipeline("ner", model="openchs/ner_distillbert_v1", aggregation_strategy="simple")

  Example of a typical helpline conversation snippet
  text = "Hello 116, my name is Mary and I'm calling from Kampala. My daughter, Jane, is 12 years old and is being
  bullied at school by a boy named Peter."

  entities = ner(text)
  for entity in entities:
      print(f"{entity['entity_group']}: {entity['word']} (score: {entity['score']:.2f})")

MLflow Tracking

—Experiment: NER_Distilbert/marlon
—Run ID: 10d2648a456a4f6ab74022a9e45c9f40
—Training Date: 2025-10-30 11:58:48
—Tracking URI: http://192.168.10.6:5000

Training Metrics Visualization

View detailed training metrics and TensorBoard logs in the Training metrics tab.

Citation

bibtex

@misc{ner_distilbert_base_cased,
  title={ner-distilbert-base-cased},
  author={OpenCHS Team},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/openchs/ner_distillbert_v1}
}

Contact

info@bitz-itc.com

Model card auto-generated from MLflow