metadata
license: mit
model-index:
- name: rembert-qlora
results: []
base_model:
- google/rembert
ReMBERT QLoRA – Grounding Act Classification
This model is a fine-tuned version of google/rembert, 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/rembert-qlora")
tokenizer = AutoTokenizer.from_pretrained("MB55/rembert-qlora")
inputs = tokenizer("Also das habe ich jetzt verstanden.", return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(dim=1).item()