GliZNet

GliZNet (Generalized Zero-Shot Network) is a zero-shot text classification model that processes the input text and all candidate labels jointly in a single forward pass, achieving O(1) inference complexity regardless of the number of labels.

Available in two backbone variants:

• alexneakameni/gliznet-deberta-v3-base — DeBERTa-v3-base backbone (~184M params)
• alexneakameni/gliznet-ModernBERT-base — ModernBERT-base backbone (~150M params)

GliZNet encodes text and labels together in one sequence, extracts each label's representation from its [LAB] separator token, builds a label-specific text summary via cross-attention, and scores each pair with a bilinear head. A hybrid loss combining one-vs-negatives softmax (primary, with additive margin), auxiliary focal loss, and a partial VICReg regularizer (variance + covariance terms only — the invariance term is dropped, leaving a pure label-repulsion objective) sharpens discrimination between semantically similar labels.

Paper: GliZNet: A Novel Architecture for Zero-Shot Text Classification Alex Kameni (Ivalua / Massy, France)

Code: github.com/KameniAlexNea/zero-shot-classification Synthetic data generation: github.com/KameniAlexNea/generate-gliznet-data

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Model Details

Architecture Summary

flowchart TD
    A["Input sequence\n[CLS] <text> [SEP] <label_1> [LAB] <label_2> [LAB] … [PAD]"]
    A --> B["DeBERTa-v3-base\nContextual hidden states"]
    B --> C["Label repr\n[LAB] token hidden state per label"]
    B --> D["Label-specific text repr\nCross-attention: label queries text tokens"]
    C --> E["Bilinear scoring head\nlogit = Bilinear(text_repr, label_repr)"]
    D --> E
    E --> F["One-vs-negatives loss (+ margin)\n+ focal loss\n+ label repulsion\n(training only)"]

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Usage

With ZeroShotClassificationPipeline

import torch
from gliznet.model import GliZNetForSequenceClassification
from gliznet.tokenizer import GliZNETTokenizer
from gliznet.predictor import ZeroShotClassificationPipeline

model = GliZNetForSequenceClassification.from_pretrained("alexneakameni/gliznet-deberta-v3-base")
model = model.to(torch.bfloat16)
tokenizer = GliZNETTokenizer.from_pretrained("alexneakameni/gliznet-deberta-v3-base")

pipeline = ZeroShotClassificationPipeline(
    model, tokenizer,
    classification_type="multi-label",   # or "multi-class"
    device="cuda",
)

text = "Scientists discover a new exoplanet orbiting a distant star."
labels = ["astronomy", "politics", "cooking", "space exploration", "finance"]

result = pipeline(text, labels)
for ls in sorted(result.labels, key=lambda x: -x.score):
    print(f"  {ls.label:<25} {ls.score:.3f}")

With from_pretrained + HuggingFace AutoModel

from transformers import AutoModel, AutoConfig
import gliznet  # triggers Auto* registration

config = AutoConfig.from_pretrained("alexneakameni/gliznet-deberta-v3-base")
model  = AutoModel.from_pretrained("alexneakameni/gliznet-deberta-v3-base")

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Performance

Evaluated on the GLiClass benchmark — 10 standard text-classification datasets reported as macro F1. GLiClass variants are the closest published competitors; all encode text and labels jointly in a single forward pass.

Macro F1 on GLiClass benchmark datasets

Δ GliZNet-ModernBERT vs GLiClass-large: −0.0783 · Δ vs GLiClass-base: −0.0422 · Δ vs GLiClass-modern-base: +0.0464 Δ GliZNet-DeBERTa vs GLiClass-large: −0.0529 · Δ vs GLiClass-base: −0.0168 · Δ vs GLiClass-modern-base: +0.0718

Both GliZNet variants are base-size models trained on synthetic data with a single-stage pipeline. GliZNet-DeBERTa surpasses GLiClass-base on AG News, Emotion, and SST-5. GliZNet-ModernBERT excels on SST-5 (+0.0991 vs GLiClass-base) and AG News (+0.0591).

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Training Details

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Tokenizer

GliZNETTokenizer wraps the DeBERTa-v3 sentencepiece tokenizer and adds a custom [LAB] separator token. The input format is:

[CLS] <text> [SEP] <label_1> [LAB] <label_2> [LAB] ... <label_n> [LAB] [PAD]*

The lmask tensor (label mask) assigns 0 to text tokens and unique integers 1…n to each label's tokens, allowing the model to pool each label independently.

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Limitations

• Trained on synthetic English data; performance on specialized domains (legal, medical) or non-English text may degrade.
• Best results when all candidate labels fit within 1024 tokens. Very large label sets should be batched.
• The model does not produce calibrated probabilities; scores are bilinear compatibility values passed through a sigmoid.

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Citation

@article{kameni2025gliznet,
  title   = {GliZNet: A Novel Architecture for Zero-Shot Text Classification},
  author  = {Alex Kameni},
  year    = {2025},
  note    = {Preprint. Code: https://github.com/KameniAlexNea/zero-shot-classification}
}

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License

Apache 2.0