|
--- |
|
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] |