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rubert-base-cased-nli-threeway

Zero-Shot Classification
Transformers
PyTorch
ONNX
Safetensors
Russian
bert
text-classification
rubert
russian
nli
rte
Inference Endpoints
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@@ -82,7 +82,7 @@ The table below shows ROC AUC for three models on small samples of the DEV sets:
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  - [twoway](https://huggingface.co/cointegrated/rubert-base-cased-nli-twoway): a base-sized BERT predicting entailment vs not_entailment
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  - [threeway](https://huggingface.co/cointegrated/rubert-base-cased-nli-threeway) (**this model**): a base-sized BERT predicting entailment vs contradiction vs neutral
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- |model |tiny/entailment|twoway/entailment|threeway/entailment|threeway[3]/contradiction|threeway[3]/neutral|
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  |-----------|---------------|-----------------|-------------------|-------------------------|-------------------|
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  |add_one_rte|0.82 |0.90 |0.92 | | |
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  |anli_r1 |0.50 |0.68 |0.66 |0.70 |0.75 |
 
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  - [twoway](https://huggingface.co/cointegrated/rubert-base-cased-nli-twoway): a base-sized BERT predicting entailment vs not_entailment
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  - [threeway](https://huggingface.co/cointegrated/rubert-base-cased-nli-threeway) (**this model**): a base-sized BERT predicting entailment vs contradiction vs neutral
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+ |model |tiny/entailment|twoway/entailment|threeway/entailment|threeway/contradiction|threeway/neutral|
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  |-----------|---------------|-----------------|-------------------|-------------------------|-------------------|
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  |add_one_rte|0.82 |0.90 |0.92 | | |
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  |anli_r1 |0.50 |0.68 |0.66 |0.70 |0.75 |