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README.md
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ModernBERT multi-task fine-tuned on tasksource NLI tasks, including MNLI, ANLI, SICK, WANLI, doc-nli, LingNLI, FOLIO, FOL-NLI, LogicNLI, Label-NLI and all datasets in the below table).
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This is the equivalent of an "instruct" version.
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Test accuracy at
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| test_name | test_accuracy |
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|:-------------------------------------|----------------:|
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| glue/mnli | 0.
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| 28 |
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| glue/qnli | 0.
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| 29 |
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| glue/rte | 0.
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| 30 |
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| super_glue/cb | 0.
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| 31 |
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| anli/a1 | 0.
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| 32 |
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| anli/a2 | 0.
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| 33 |
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| anli/a3 | 0.
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| 34 |
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| sick/label | 0.
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| 35 |
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| sick/entailment_AB | 0.
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| 36 |
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| snli | 0.
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| 37 |
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| scitail/snli_format | 0.
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| 38 |
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| hans |
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| 39 |
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| WANLI | 0.
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| recast/
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| recast/
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| recast/
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| recast/
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| recast/
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| recast/
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| recast/
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| recast/recast_megaveridicality | 0.
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| probability_words_nli/
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| probability_words_nli/reasoning_1hop | 0.
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| probability_words_nli/
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| 51 |
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| nan-nli | 0.
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| 52 |
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| nli_fever | 0.
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| 53 |
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| breaking_nli |
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| 54 |
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| conj_nli | 0.
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| 55 |
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| fracas | 0
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| 56 |
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| dialogue_nli | 0.
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| 57 |
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| mpe | 0.
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| 58 |
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| dnc | 0.
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| 59 |
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| recast_white/fnplus | 0.
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| 60 |
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| recast_white/sprl | 0.
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| 61 |
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| recast_white/dpr | 0.
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| 62 |
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| robust_nli/IS_CS | 0.
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| 63 |
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| robust_nli/LI_LI | 0.
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| 64 |
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| robust_nli/ST_WO | 0.
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| 65 |
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| robust_nli/PI_SP | 0.
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| 66 |
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| robust_nli/PI_CD | 0.
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| 67 |
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| robust_nli/ST_SE | 0.
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| 68 |
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| robust_nli/ST_NE | 0.
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| 69 |
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| robust_nli/ST_LM | 0.
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| 70 |
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| robust_nli_is_sd |
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| 71 |
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| robust_nli_li_ts | 0.
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| 72 |
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| add_one_rte | 0.
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| 73 |
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| cycic_classification | 0.
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| 74 |
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| lingnli | 0.
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| 75 |
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| monotonicity-entailment | 0.
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| 76 |
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| scinli | 0.
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| 77 |
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| naturallogic | 0.
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| 78 |
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| syntactic-augmentation-nli | 0.
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| 79 |
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| autotnli | 0.
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| 80 |
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| defeasible-nli/atomic | 0.
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| 81 |
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| defeasible-nli/snli | 0.
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| 82 |
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| help-nli | 0.
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| 83 |
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| nli-veridicality-transitivity | 0.
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| lonli | 0.
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| 85 |
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| dadc-limit-nli | 0.
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| 86 |
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| folio | 0.
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| 87 |
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| tomi-nli | 0.
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| 88 |
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| temporal-nli | 0.
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| counterfactually-augmented-snli | 0.
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| cnli | 0.
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| 91 |
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| logiqa-2.0-nli | 0.
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| 92 |
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| mindgames | 0.
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| 93 |
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| ConTRoL-nli | 0.
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| 94 |
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| logical-fallacy | 0.
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| conceptrules_v2 | 0.
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| zero-shot-label-nli | 0.
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| scone | 0.
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| 98 |
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| monli | 0.
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| SpaceNLI |
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| 100 |
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| propsegment/nli | 0.
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| SDOH-NLI |
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| 102 |
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| scifact_entailment | 0.
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| 103 |
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| AdjectiveScaleProbe-nli | 0.
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| 104 |
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| resnli | 0.
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| semantic_fragments_nli | 0.
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| dataset_train_nli | 0.
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| ruletaker | 0.
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| PARARULE-Plus | 1 |
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| logical-entailment | 0.
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| nope | 0.
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| LogicNLI | 0.
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| contract-nli/contractnli_a/seg | 0.
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| contract-nli/contractnli_b/full | 0.
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| nli4ct_semeval2024 | 0.
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| biosift-nli | 0.
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| SIGA-nli | 0.
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| FOL-nli | 0.
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| doc-nli | 0.
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| mctest-nli | 0.
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| idioms-nli | 0.
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| lifecycle-entailment | 0.
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| MSciNLI | 0.
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# Usage
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ModernBERT multi-task fine-tuned on tasksource NLI tasks, including MNLI, ANLI, SICK, WANLI, doc-nli, LingNLI, FOLIO, FOL-NLI, LogicNLI, Label-NLI and all datasets in the below table).
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This is the equivalent of an "instruct" version.
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Test accuracy at 100k training steps. 250k steps version coming around 25 december.
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| test_name | test_accuracy |
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|:-------------------------------------|----------------:|
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| 27 |
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| glue/mnli | 0.91 |
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| 28 |
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| glue/qnli | 0.93 |
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| 29 |
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| glue/rte | 0.86 |
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| super_glue/cb | 0.89 |
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| 31 |
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| anli/a1 | 0.62 |
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| 32 |
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| anli/a2 | 0.47 |
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| 33 |
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| anli/a3 | 0.42 |
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| sick/label | 0.92 |
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| sick/entailment_AB | 0.84 |
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| snli | 0.91 |
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| scitail/snli_format | 0.95 |
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| hans | 1 |
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| WANLI | 0.71 |
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| recast/recast_sentiment | 0.98 |
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| recast/recast_verbcorner | 0.94 |
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| recast/recast_ner | 0.87 |
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| recast/recast_factuality | 0.93 |
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| recast/recast_puns | 0.93 |
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| recast/recast_kg_relations | 0.94 |
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| recast/recast_verbnet | 0.88 |
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| recast/recast_megaveridicality | 0.87 |
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| probability_words_nli/usnli | 0.77 |
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| probability_words_nli/reasoning_1hop | 0.99 |
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| probability_words_nli/reasoning_2hop | 0.9 |
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| nan-nli | 0.85 |
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| nli_fever | 0.72 |
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| breaking_nli | 1 |
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| conj_nli | 0.71 |
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| fracas | 0.86 |
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| dialogue_nli | 0.88 |
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| mpe | 0.73 |
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| dnc | 0.9 |
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| recast_white/fnplus | 0.81 |
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| recast_white/sprl | 0.92 |
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| recast_white/dpr | 0.61 |
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| robust_nli/IS_CS | 0.76 |
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| robust_nli/LI_LI | 0.98 |
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| robust_nli/ST_WO | 0.85 |
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| robust_nli/PI_SP | 0.74 |
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| robust_nli/PI_CD | 0.8 |
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| robust_nli/ST_SE | 0.78 |
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| robust_nli/ST_NE | 0.86 |
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| robust_nli/ST_LM | 0.81 |
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| robust_nli_is_sd | 1 |
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| robust_nli_li_ts | 0.91 |
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| add_one_rte | 0.91 |
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| cycic_classification | 0.83 |
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| lingnli | 0.82 |
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| monotonicity-entailment | 0.95 |
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| scinli | 0.79 |
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| naturallogic | 0.91 |
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| syntactic-augmentation-nli | 0.95 |
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| autotnli | 0.92 |
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| defeasible-nli/atomic | 0.76 |
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| defeasible-nli/snli | 0.79 |
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| help-nli | 0.91 |
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| nli-veridicality-transitivity | 0.99 |
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| 84 |
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| lonli | 0.99 |
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| dadc-limit-nli | 0.67 |
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| folio | 0.59 |
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| tomi-nli | 0.53 |
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| temporal-nli | 0.92 |
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| counterfactually-augmented-snli | 0.74 |
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| cnli | 0.81 |
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| logiqa-2.0-nli | 0.57 |
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| mindgames | 0.94 |
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| ConTRoL-nli | 0.65 |
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| logical-fallacy | 0.31 |
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| conceptrules_v2 | 0.99 |
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| zero-shot-label-nli | 0.74 |
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| scone | 0.97 |
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| monli | 0.98 |
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| SpaceNLI | 1 |
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| propsegment/nli | 0.91 |
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| SDOH-NLI | 1 |
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| 102 |
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| scifact_entailment | 0.78 |
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| 103 |
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| AdjectiveScaleProbe-nli | 0.99 |
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| 104 |
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| resnli | 0.99 |
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| 105 |
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| semantic_fragments_nli | 0.99 |
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| dataset_train_nli | 0.88 |
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| ruletaker | 0.91 |
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| PARARULE-Plus | 1 |
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| logical-entailment | 0.73 |
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| nope | 0.54 |
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| LogicNLI | 0.65 |
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| contract-nli/contractnli_a/seg | 0.87 |
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| contract-nli/contractnli_b/full | 0.78 |
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| nli4ct_semeval2024 | 0.6 |
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| biosift-nli | 0.88 |
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| SIGA-nli | 0.54 |
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| FOL-nli | 0.71 |
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| doc-nli | 0.82 |
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| mctest-nli | 0.89 |
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| idioms-nli | 0.86 |
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| lifecycle-entailment | 0.71 |
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| MSciNLI | 0.82 |
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| hover-3way/nli | 0.9 |
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| seahorse_summarization_evaluation | 0.82 |
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| babi_nli | 0.94 |
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| gen_debiased_nli | 0.9 |
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# Usage
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