MoritzLaurer/deberta-v3-base-zeroshot-v2.0
Primitive: /extract · Extract ·
DeBERTa
Models in this series are designed for efficient zeroshot classification with the Hugging Face pipeline. These models can do classification without training data and run on both GPUs and CPUs. An overview of the latest zeroshot classifiers is available in my Zeroshot Classifier Collection.
View on Hugging Face → Fine-tuned from microsoft/deberta-v3-base
Overview
Hardware: — drives latency, throughput & cost
| Size | 184M params |
|---|---|
| Tasks | /extract |
| License | mit |
| Languages | en |
| Latency | — |
| Throughput | — |
| Cost | — /1M tok |
Cost is approximate — computed from list GPU prices; your actual price depends on the provider you deploy SIE with.
Extraction
| Output kinds | Class Labels |
|---|---|
| Inputs | text |
| Max sequence length | 512 |
Benchmarks
AG News
Topic classification of news articles into world, sports, business, and sci/tech categories
Corpus: 7,600 Queries: 7,600
Quality
accuracy 0.8814
medical_questions_pairs
Classify whether two medical questions ask the same thing (question-pair similarity)
Corpus: 3,048 Queries: 3,048
Quality
accuracy 0.4826
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