| | --- |
| | language: en |
| | tags: |
| | - vad |
| | - emotion |
| | - bert |
| | license: mit |
| | model-index: |
| | - name: vad-bert |
| | results: [] |
| | datasets: |
| | - reallycarlaost/emobank |
| | base_model: |
| | - google-bert/bert-base-uncased |
| | --- |
| | |
| | # vad-bert |
| |
|
| | A BERT-based model fine-tuned to predict **Valence, Arousal, and Dominance (VAD)** values from text. |
| |
|
| | ## Intended use |
| |
|
| | This model is intended for regression tasks on emotional dimensions. It outputs 3 float values corresponding to: |
| |
|
| | - Valence (pleasant vs unpleasant) |
| | - Arousal (calm vs excited) |
| | - Dominance (controlled vs in control) |
| |
|
| | ## Example |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("RobroKools/vad-bert") |
| | model = AutoModelForSequenceClassification.from_pretrained("RobroKools/vad-bert") |
| | |
| | inputs = tokenizer("I'm feeling great!", return_tensors="pt") |
| | outputs = model(**inputs) |
| | |
| | vad = outputs.logits.detach().squeeze().tolist() |
| | print(vad) # [valence, arousal, dominance] |
