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GitHub / eskinderit / A-comparison-of-small-and-large-uni-multi-modal-language-models-for-sentiment-analysis-

Comparison of multimodal models for Emotion Detection on IEMOCAP

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Stars: 0
Forks: 0
Open issues: 0

License: None
Language: Jupyter Notebook
Size: 25.7 MB
Dependencies parsed at: Pending

Created at: over 2 years ago
Updated at: almost 2 years ago
Pushed at: almost 2 years ago
Last synced at: over 1 year ago

Commit Stats

Commits: 127
Authors: 5
Mean commits per author: 25.4
Development Distribution Score: 0.591
More commit stats: https://commits.ecosyste.ms/hosts/GitHub/repositories/eskinderit/A-comparison-of-small-and-large-uni-multi-modal-language-models-for-sentiment-analysis-

Topics: albert, analysis, attention, bilstm, chroma, class, f1, iemocap, machine-learning, mel, mfcc, models, multimodal, nlp, performance, sentiment, sentiment-analysis, sentiment-classification, weights, zcr

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