hyperquest/atom-classifier

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

• —hyperbase -- Semantic Hypergraph toolkit
• —hyperbase-parser-ab -- Alpha-Beta parser