metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- text-classification
- multi-label-classification
- bert
- conversational-qa
- educational-ai
language: en
metrics:
- f1
- precision
- recall
Enhanced BERT Multi-Label Classifier for Conversational QA
Performance
- Micro F1: 0.7779
- Macro F1: 0.7098
- Optimal Threshold: 0.20 (CRITICAL - not 0.5!)
Class Performance
| Label | F1 Score | Status |
|---|---|---|
| questioning | 0.933 | Excellent |
| responsive | 0.756 | Good |
| interactive | 0.613 | Good (breakthrough!) |
| collaborative | 0.537 | Acceptable |
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained("IIchukissII/enhanced-bert-coqa-multilabel-classifier")
tokenizer = AutoTokenizer.from_pretrained("IIchukissII/enhanced-bert-coqa-multilabel-classifier")
text = "context [SEP] question [SEP] answer"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.sigmoid(outputs.logits)
# IMPORTANT: Use threshold 0.20, not 0.5!
LABELS = ["interactive", "responsive", "questioning", "collaborative"]
predictions = {label: int(probs[0, i] >= 0.20) for i, label in enumerate(LABELS)}
Architecture
- Enhanced BERT with 3-layer classifier head
- Layer normalization and L2 regularization
- Optimized for multi-label classification
Training
- 7 epochs on expanded dataset
- Breakthrough in interactive detection (+15.7% F1)
- Threshold optimization discovery (0.20 optimal)