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rembert-qlora
MB55/rembert-qlora
rembert-qlora is a machine learning model from MB55. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This model is a fine-tuned version of google/rembert, optimized using QLoRA for efficient binary classification of German dialogue utterances into:
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Public
Updated Aug 7, 2025
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.json33.4 MB · 54%
At a glance
- License
- mit
- Access
- Public
- Created
- May 3, 2025
- Updated
- Aug 7, 2025
- SHA
- 105b076d
Base models
Model card
From the Hugging Face model README
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()
- License
- mit
- Created
- May 3, 2025
- Updated
- Aug 7, 2025