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