M1-Lite Paper Embedding Model ONNX
This is a compact student encoder distilled from PeytonT/1m-paper-embedding-model over the 1M paper corpus.
It predicts the same 768-dimensional, L2-normalized M1 embedding space used by the Research Library paper universe.
Files
| Path | Description |
|---|---|
onnx/model.onnx |
Float ONNX student encoder. |
onnx/model.int8.onnx |
Dynamically quantized int8 ONNX model for browser/WASM inference. |
tokenizer/ |
Student tokenizer files. |
manifest.json |
Export metadata consumed by the static viewer. |
Training
- teacher target:
exports/huggingface/paper_universe_interactive_v1/semantic_m1/papers_all.emb.i8 - interactive level:
exports/huggingface/paper_universe_interactive_v1/interactive/papers_all.json - rows:
1,000,000 - final eval cosine vs M1 targets:
0.7351 - final eval MSE vs M1 targets:
0.000690 - int8 ONNX size:
33.7 MB
Embedding Format
- output name:
embedding - shape:
[batch, 768] - pooling: attention-mask mean pooling, projection to 768 dimensions, L2 normalization
- intended max sequence length:
128
The output is compatible with the existing semantic_m1/*.emb.i8 paper-vector files.
Model tree for PeytonT/1m-paper-embedding-model-lite-onnx
Base model
google/bert_uncased_L-4_H-256_A-4