Atom Classifier
A multilingual token classifier for semantic hypergraph parsing. It classifies each token in a sentence into one of 39 semantic atom types/subtypes, serving as the first stage (alpha) of the Alpha-Beta semantic hypergraph parser.
Model Details
- Architecture: DistilBertForTokenClassification
- Base model: distilbert-base-multilingual-cased
- Labels: 39 semantic atom types
- Max sequence length: 512
Label Taxonomy
Atoms are typed according to the Semantic Hyperedge (SH) notation system. The 7 main types and their subtypes:
Concepts (C)
Predicates (P)
Modifiers (M)
Builders (B)
Triggers (T)
Conjunctions (J)
Special
Usage
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("hyperquest/atom-classifier")
model = AutoModelForTokenClassification.from_pretrained("hyperquest/atom-classifier")
sentence = "Berlin is the capital of Germany."
encoded = tokenizer(sentence, return_tensors="pt", return_offsets_mapping=True)
offset_mapping = encoded.pop("offset_mapping")
with torch.no_grad():
outputs = model(**encoded)
predictions = outputs.logits.argmax(-1)[0].tolist()
word_ids = encoded.word_ids(0)
for idx, word_id in enumerate(word_ids):
if word_id is not None:
start, end = offset_mapping[0][idx].tolist()
label = model.config.id2label[predictions[idx]]
print(f"{sentence[start:end]:15s} -> {label}")
Intended Use
This model is designed to be used as the first stage of the Alpha-Beta semantic hypergraph parser (hyperbase-parser-ab). It assigns atom types to tokens, which are then combined into nested hypergraph structures by rule-based grammar in the beta stage.
Part of
