ATLOP RoBERTa-base ONNX (edgextract)
Document-level relation extraction head trained for the edgextract browser demo
(Wikidata relations mode). The encoder is roberta-base; the head follows
ATLOP (Zhou et al., AAAI 2021): entity markers, localized context pooling,
grouped bilinear classifier, adaptive-threshold loss.
Files
| Path | Role |
|---|---|
model.onnx |
Full ATLOP forward (encoder + head), opset 17 |
config.json |
rel2id, margin shift, decision-model band params |
rel_info.json |
Wikidata P-code → English label / description |
tokenizer* / vocab.json / merges.txt |
RoBERTa tokenizer (same as training) |
ONNX inputs / output
input_ids[1, seq],attention_mask[1, seq]entity_starts[E, M]int64,-1for unused mention slots (start-marker indices)hts[P, 2]int64 entity-pair indices- output
logits[P, 97](class 0 = threshold / NA, then 96 Wikidata relations)
Training data
Fine-tuned on Re-DocRED train only (train_revised.json). Dev selected the
checkpoint and margin shift; test was opened once for the published comparison in
the edgextract repo. Do not treat browser extractions as comparable to that
score: the benchmark supplies gold entity clusters; the demo finds names from
raw text.
How to use in the demo
- Open the edgextract web demo.
- Step 2 → Wikidata relations (RoBERTa).
- Load RoBERTa (default Hub id:
raphaelmansuy/atlop-roberta-base-onnx). - Extract. Uncertain pairs are verified by the selected decision backend (Tev1 WebGPU or Ollama).
Local export (from the repo):
make demo-atlop-export
make demo-atlop-publish # this card + weights → Hugging Face
License
- This repository: Apache-2.0
- Base encoder FacebookAI/roberta-base: MIT
- Re-DocRED: see Tan et al., EMNLP 2022 / the dataset card for data terms