Model Card for Model ID
This model was fine-tuned for emotional analysis of texts, more precisely
the prediction of emotion dimensions Active, Valuation, and Dominance.
Training was conducted on the transcripts of MSP Podcast 2.0,
a large naturalistic speech emotion corpus
(Busso et al 2025).
The pretrained model is
roberta-large, and
Low-Rank
Adaptaters (LoRA) were
used for efficiency (compute, storage, and implicit regularization).
The model was made within the AstroSpeech project of the
MTA TTK Environmental Adaptation and Space Research
Group.
MSP Podcast is annotated for both emotion class and dimensions.
There are 8 emotion classes proper along with X (for annotator disagreement) and
O (for other emotions).
The examples belonging to these two special classes were
excluded from fine-tuning.
The emotion dimensions were rescaled with sklearn's StandardScaler.
The sequence representations are computed as the mean of the tokens.
The intermediate layer in the emotion dimension regressor is a ReLU
(instead of Dropout, wich is, as far as the author knows, transformers's default
for sequence classification).
LoRA rank is 2, and the training loss is weighted by the inverse frequency of
the emotion class of the training sample.
Model Details
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roberta-large
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