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--- |
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license: mit |
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model-index: |
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- name: rembert-qlora |
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results: [] |
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base_model: |
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- google/rembert |
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--- |
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# ReMBERT QLoRA – Grounding Act Classification |
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This model is a fine-tuned version of [google/rembert](https://huggingface.co/google/rembert), optimized using QLoRA for efficient binary classification of German dialogue utterances into: |
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- `ADVANCE`: Contribution that moves the dialogue forward (e.g. confirmations, follow-ups, elaborations) |
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- `NON-ADVANCE`: Other utterances (e.g. vague responses, misunderstandings, irrelevant comments) |
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## Use Cases |
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- Dialogue system analysis |
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- Teacher-student interaction classification |
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- Grounding in institutional advising or classroom discourse |
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## How to Use |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("MB55/rembert-qlora") |
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tokenizer = AutoTokenizer.from_pretrained("MB55/rembert-qlora") |
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inputs = tokenizer("Also das habe ich jetzt verstanden.", return_tensors="pt") |
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outputs = model(**inputs) |
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predicted_class = outputs.logits.argmax(dim=1).item() |
