vad-bert (ONNX + int8, Psycheros builds)

ONNX exports of RobroKools/vad-bert (BERT-base fine-tuned on EmoBank for Valence/Arousal/Dominance regression), prepared for transformers.js-style in-process inference — no PyTorch, no server.

Two variants, identical behavior:

variant	size	notes
int8/	~110MB	dynamic int8 quantization; ~2–2.6× faster on CPU, ~0.02 delta drift
fp32/	~438MB	bit-exact conversion of the original weights

Each folder is a standalone transformers.js layout: tokenizer/config at the root, onnx/model.onnx single-file. In both variants the model file is named onnx/model.onnx regardless of precision.

What was done to the weights

• fp32/ — ONNX export of the original pytorch_model.bin via optimum (format conversion only; weights unchanged, verified output-identical).
• int8/ — dynamic int8 quantization of that export via onnxruntime (quantize_dynamic, QInt8).
• Tokenizer and config files are re-serialized by the export tooling — semantically identical (same tokens, same ids, same model config), but the JSON shape differs from the original repo's (newer transformers serializes special tokens in expanded form). tokenizer.json and vocab.txt are byte-identical to the original.

Credits & license

• Original model: RobroKools/vad-bert — MIT
• Base model: google-bert/bert-base-uncased — Apache 2.0
• Training data: EmoBank (via reallycarlaost/emobank)

This repository is distributed under the same MIT license as the original; see LICENSE and the original's license above.