metadata
base_model: flair/bueble-lm-2b
license: mit
BübleLM QLoRA – Grounding Act Classification
This model is a fine-tuned version of flair/bueble-lm-2b, optimized using QLoRA for efficient binary classification of German dialogue utterances into:
- advance: Contribution that moves the dialogue forward (e.g. confirmations, follow-ups, elaborations)
- non_advance: Other utterances (e.g. vague responses, misunderstandings, irrelevant comments)
Use Cases
- Dialogue system analysis
- Teacher-student interaction classification
- Grounding in institutional advising or classroom discourse
How to Use
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("MB55/bueble-classifier")
tokenizer = AutoTokenizer.from_pretrained("MB55/bueble-classifier")
inputs = tokenizer("Can you explain it again?", return_tensors="pt")
outputs = model(**inputs)
prediction = outputs.logits.argmax(dim=-1)
print(prediction) # 0 = non_advance, 1 = advance