EmotiSpace 128 EmotiSpace 128 is a BERT based hybrid emotion model that maps text into a 128 dimensional emotional embedding space and also predicts GoEmotions style emotion labels. It is not only a classifier. The main goal is to produce a reusable emotional latent space where similar emotional meanings are close together, while still exposing readable emotion probabilities. What it does Input: Output: a normalized 128D emotion embedding raw logits sigmoid probabilities for emotion labels Example labels: Architecture The model uses a custom Transformers architecture: Load it with trust remote code=True . Training The model was trained in two passes. Pass 1 focused on emotion classification using GoEmotions labels. Pass 2 continued training with a combined objective: The second pass was used to make the 128D embedding space more useful for cosine similarity and custom emotion anchors. Training used the last 4 BERT layers unfrozen, with early stopping. Intended use EmotiSpace 128 is useful for: emotion classification emotion embeddings emotional similarity search custom emotion anchors beyond fixed labels character mood systems dialogue tone control TTS/prosody control pipelines con…
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