Text Classification
Transformers
PyTorch
TensorBoard
English
distilbert
qa-metrics
call-center
multi-head
transcript-analysis
customer-service
quality-assurance
child-helplines
crisis-support
social-impact
swahili
east-africa
Eval Results (legacy)
Instructions to use openchs/qa-helpline-distilbert-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openchs/qa-helpline-distilbert-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="openchs/qa-helpline-distilbert-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, MultiHeadQAClassifier tokenizer = AutoTokenizer.from_pretrained("openchs/qa-helpline-distilbert-v1") model = MultiHeadQAClassifier.from_pretrained("openchs/qa-helpline-distilbert-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download metadata.json from openchs/qa-helpline-distilbert-v1: direct link, hf CLI and curl.
- Browser
- Download file 2.15 kB
-
https://huggingface.co/openchs/qa-helpline-distilbert-v1/resolve/main/metadata.json
- Command line
-
hf download hf://openchs/qa-helpline-distilbert-v1/metadata.json
-
curl -L -o metadata.json https://huggingface.co/openchs/qa-helpline-distilbert-v1/resolve/main/metadata.json
2.15 kB
| { | |
| "mlflow": { | |
| "run_info": { | |
| "run_id": "a86060a18b304d8b87f5606c94584ff2", | |
| "experiment_id": "12", | |
| "experiment_name": "QA_Multihead_Clasification_Model", | |
| "start_time": "2025-07-14 12:27:30", | |
| "end_time": "2025-07-14 12:27:35", | |
| "status": "FINISHED", | |
| "artifact_uri": "/opt/chl_ai/mlflow-shared/artifacts/12/a86060a18b304d8b87f5606c94584ff2/artifacts" | |
| }, | |
| "metrics": { | |
| "opening_accuracy": 0.9444444444444444, | |
| "opening_precision": 0.9444444444444444, | |
| "opening_recall": 0.9444444444444444, | |
| "opening_f1_score": 0.9444444444444444, | |
| "listening_accuracy": 0.2777777777777778, | |
| "listening_precision": 0.7101449275362319, | |
| "listening_recall": 0.9245283018867925, | |
| "listening_f1_score": 0.8032786885245902, | |
| "proactiveness_accuracy": 0.6666666666666666, | |
| "proactiveness_precision": 0.6956521739130435, | |
| "proactiveness_recall": 0.9411764705882353, | |
| "proactiveness_f1_score": 0.8, | |
| "resolution_accuracy": 0.16666666666666666, | |
| "resolution_precision": 0.7213114754098361, | |
| "resolution_recall": 0.8979591836734694, | |
| "resolution_f1_score": 0.8, | |
| "hold_accuracy": 0.8333333333333334, | |
| "hold_precision": 0.0, | |
| "hold_recall": 0.0, | |
| "hold_f1_score": 0.0, | |
| "closing_accuracy": 0.8888888888888888, | |
| "closing_precision": 0.8888888888888888, | |
| "closing_recall": 0.8888888888888888, | |
| "closing_f1_score": 0.8888888888888888 | |
| }, | |
| "params": { | |
| "num_epochs": "2", | |
| "learning_rate": "2e-05", | |
| "batch_size": "2", | |
| "max_length": "512", | |
| "qa_heads_config": "{'opening': 1, 'listening': 5, 'proactiveness': 3, 'resolution': 5, 'hold': 2, 'closing': 1}" | |
| }, | |
| "tags": { | |
| "mlflow.user": "rogendo", | |
| "mlflow.source.name": "/home/rogendo/.virtualenvs/mcreativity/lib/python3.12/site-packages/ipykernel_launcher.py", | |
| "mlflow.source.type": "LOCAL", | |
| "mlflow.runName": "Trained MultiHead QA Model" | |
| } | |
| }, | |
| "user": {}, | |
| "export": { | |
| "method": "unknown", | |
| "timestamp": "2025-10-01T16:32:06.634315", | |
| "version": "1", | |
| "mode": "metrics_only" | |
| } | |
| } |