deberta-large-mnli-zero-cls

Model overview

deberta-large-mnli-zero-cls is published by Narsil as a DeBERTa-based zero-shot-classification model. The captured configuration identifies DebertaForSequenceClassification with a deberta model type. According to the model card, this is the DeBERTa large model fine-tuned with the MNLI task.

Recorded capabilities

Use cases in the source record

• Zero-shot classification workflows using the captured zero-shot-classification pipeline tag and text-classification tagging.
• NLI-grounded classification experiments building on the publisher's documented MNLI fine-tune.

Limitations and unknowns

• No parameter count was extracted from this record.
• No scored evaluation results were extracted from this record; the card mentions SQuAD and GLUE dev results without numeric scores.
• No context-window value was extracted from this record.
• Provider state is historical snapshot data, not independently refreshed current availability.

Source and provenance

Source: Narsil/deberta-large-mnli-zero-cls

Captured: Unknown. Processed: 2026-09-07T19:34:34.817889+00:00.

DeBERTa: Decoding-enhanced BERT with Disentangled Attention DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data. Please check the official repository for more details and updates. This is the DeBERTa large model fine-tuned with MNLI task. Fine-tuning on NLU tasks We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks. Model SQuAD 1.1 SQuAD 2.0 MNLI-m/mm SST-2 QNLI CoLA RTE MRPC QQP STS-B F1/EM F1/EM Acc Acc Acc MCC Acc Acc/F1 Acc/F1 P/S BERT-Large 90.9/84.1 81.8/79.0 86.6/- 93.2 92.3…