ghotriw/deberta-v3-xsmall-biaffine-dep-pos-feats-en-ewt-gum

ModernBiaffineParser — microsoft/deberta-v3-xsmall

Biaffine dependency parser + joint UPOS + morphological features (FEATS) tagger trained on Universal Dependencies English Web Treebank (EWT) and Universal Dependencies English GUM.

Encoder: microsoft/deberta-v3-xsmall (frozen weights not included — loaded from HuggingFace at runtime) Custom head: biaffine_head.safetensors — word projection, arc/rel/POS/FEATS MLPs and biaffine layers Labels: 53 DEPREL labels · 19 UPOS tags · 21 FEATS categories Score convention: s_arc[dep, head], s_rel[dep, head, rel]

Metrics (EWT + GUM, decode: Eisner (projective MST))

Split	LAS	UPOS	UFEATS	UCM	LCM
dev	92.98%	98.09%	95.66%	70.61%	60.40%
test	93.01%	98.08%	95.76%	70.63%	61.37%

ONNX / TorchScript / production use

model.onnx — fp32 ONNX model (Recommended for CPU inference). model.fp16.onnx — fp16 ONNX model (For GPU or environments with native fp16 support, ~139 MB). traced_model.pt — TorchScript model (For tch-rs or PyTorch C++ API).

Inputs: subwords [B, W, 20] int64. Outputs: s_arc [B,W,W], s_rel [B,W,W,R], s_pos [B,W,P], s_feats [B,W,C,Vmax].

# download only inference artifacts
hf download ghotriw/deberta-v3-xsmall-biaffine-dep-pos-feats-en-ewt-gum \
  model.fp16.onnx model.onnx traced_model.pt vocabs.json tokenizer.json \
  --local-dir ./model

vocabs.json — DEPREL and UPOS vocabularies (str→int dicts). feats_vocab — morphological categories {category: {value: idx}} (idx 0 = _/absent). s_feats[..., c, :] is an independent softmax per category; non-existent value slots carry -inf, so argmax over the last dim is always valid.

Input format

The model expects a word-level subword grid [B, W, fix_len=20] (int64), where each word is independently tokenised with the encoder's sentencepiece tokeniser and padded/truncated to 20 subword slots. Position 0 is a synthetic ROOT word whose only subword is [CLS] (id 1).

Vocabularies

config.json contains rel_vocab (str→int) and pos_vocab (str→int). Index 0 is the <pad> / ROOT slot and should be ignored in evaluation.

For FEATS models, config.json also contains feats_vocab {category: {value: idx}} and feats_sizes (per-category value counts, incl. the _/absent slot at index 0). The 4th output s_feats [B,W,C,Vmax] is decoded per category via argmax over its last dimension.