End of training
Browse files- README.md +65 -0
- all_results.json +11 -0
- config.json +1059 -0
- eval_results.json +11 -0
- model.safetensors +3 -0
- runs/May12_11-38-12_Nogas-MacBookPro.local/events.out.tfevents.1715503093.Nogas-MacBookPro.local.3662.6 +3 -0
- runs/May12_11-38-12_Nogas-MacBookPro.local/events.out.tfevents.1715503253.Nogas-MacBookPro.local.3662.7 +3 -0
- train_results.json +11 -0
- trainer_state.json +54 -0
- training_args.bin +3 -0
README.md
ADDED
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1 |
+
---
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+
language:
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+
- en
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+
license: apache-2.0
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+
base_model: sileod/deberta-v3-base-tasksource-nli
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: results
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results: []
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+
---
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+
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+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+
should probably proofread and complete it, then remove this comment. -->
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+
# results
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+
This model is a fine-tuned version of [sileod/deberta-v3-base-tasksource-nli](https://huggingface.co/sileod/deberta-v3-base-tasksource-nli) on an unknown dataset.
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+
It achieves the following results on the evaluation set:
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- Loss: 0.0000
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- Accuracy: 1.0
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- Precision: 1.0
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- Recall: 1.0
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- F1: 1.0
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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+
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 0
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3
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### Training results
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### Framework versions
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- Transformers 4.38.2
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- Pytorch 2.1.1
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- Datasets 2.15.0
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- Tokenizers 0.15.2
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all_results.json
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{
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"epoch": 3.0,
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"eval_Accuracy": 1.0,
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"eval_F1": 1.0,
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"eval_Precision": 1.0,
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"eval_Recall": 1.0,
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"eval_loss": 1.5955849448801018e-05,
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"eval_runtime": 1.5082,
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"eval_samples_per_second": 100.784,
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"eval_steps_per_second": 12.598
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}
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config.json
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548 |
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"tasks": [
|
549 |
+
"glue/mnli",
|
550 |
+
"glue/qnli",
|
551 |
+
"glue/rte",
|
552 |
+
"glue/wnli",
|
553 |
+
"glue/mrpc",
|
554 |
+
"glue/qqp",
|
555 |
+
"glue/stsb",
|
556 |
+
"super_glue/boolq",
|
557 |
+
"super_glue/cb",
|
558 |
+
"super_glue/multirc",
|
559 |
+
"super_glue/wic",
|
560 |
+
"super_glue/axg",
|
561 |
+
"anli/a1",
|
562 |
+
"anli/a2",
|
563 |
+
"anli/a3",
|
564 |
+
"sick/label",
|
565 |
+
"sick/relatedness",
|
566 |
+
"sick/entailment_AB",
|
567 |
+
"snli",
|
568 |
+
"scitail/snli_format",
|
569 |
+
"hans",
|
570 |
+
"WANLI",
|
571 |
+
"recast/recast_verbnet",
|
572 |
+
"recast/recast_kg_relations",
|
573 |
+
"recast/recast_ner",
|
574 |
+
"recast/recast_factuality",
|
575 |
+
"recast/recast_puns",
|
576 |
+
"recast/recast_megaveridicality",
|
577 |
+
"recast/recast_sentiment",
|
578 |
+
"recast/recast_verbcorner",
|
579 |
+
"probability_words_nli/usnli",
|
580 |
+
"probability_words_nli/reasoning_1hop",
|
581 |
+
"probability_words_nli/reasoning_2hop",
|
582 |
+
"nan-nli/joey234--nan-nli",
|
583 |
+
"nli_fever",
|
584 |
+
"breaking_nli",
|
585 |
+
"conj_nli",
|
586 |
+
"fracas",
|
587 |
+
"dialogue_nli",
|
588 |
+
"mpe",
|
589 |
+
"dnc",
|
590 |
+
"recast_white/fnplus",
|
591 |
+
"recast_white/sprl",
|
592 |
+
"recast_white/dpr",
|
593 |
+
"joci",
|
594 |
+
"robust_nli/IS_CS",
|
595 |
+
"robust_nli/LI_LI",
|
596 |
+
"robust_nli/ST_WO",
|
597 |
+
"robust_nli/PI_SP",
|
598 |
+
"robust_nli/PI_CD",
|
599 |
+
"robust_nli/ST_SE",
|
600 |
+
"robust_nli/ST_NE",
|
601 |
+
"robust_nli/ST_LM",
|
602 |
+
"robust_nli_is_sd",
|
603 |
+
"robust_nli_li_ts",
|
604 |
+
"add_one_rte",
|
605 |
+
"imppres/implicature_numerals_10_100/log",
|
606 |
+
"imppres/implicature_connectives/log",
|
607 |
+
"imppres/implicature_modals/log",
|
608 |
+
"imppres/implicature_gradable_verb/log",
|
609 |
+
"imppres/implicature_gradable_adjective/log",
|
610 |
+
"imppres/implicature_numerals_2_3/log",
|
611 |
+
"imppres/implicature_quantifiers/log",
|
612 |
+
"glue_diagnostics/diagnostics",
|
613 |
+
"hlgd",
|
614 |
+
"paws/labeled_final",
|
615 |
+
"paws/labeled_swap",
|
616 |
+
"medical_questions_pairs",
|
617 |
+
"conll2003/pos_tags",
|
618 |
+
"conll2003/chunk_tags",
|
619 |
+
"conll2003/ner_tags",
|
620 |
+
"hh-rlhf",
|
621 |
+
"model-written-evals",
|
622 |
+
"truthful_qa/multiple_choice",
|
623 |
+
"fig-qa",
|
624 |
+
"bigbench/physical_intuition",
|
625 |
+
"bigbench/authorship_verification",
|
626 |
+
"bigbench/implicit_relations",
|
627 |
+
"bigbench/dyck_languages",
|
628 |
+
"bigbench/novel_concepts",
|
629 |
+
"bigbench/moral_permissibility",
|
630 |
+
"bigbench/metaphor_understanding",
|
631 |
+
"bigbench/temporal_sequences",
|
632 |
+
"bigbench/sports_understanding",
|
633 |
+
"bigbench/analytic_entailment",
|
634 |
+
"bigbench/social_support",
|
635 |
+
"bigbench/emoji_movie",
|
636 |
+
"bigbench/dark_humor_detection",
|
637 |
+
"bigbench/suicide_risk",
|
638 |
+
"bigbench/fact_checker",
|
639 |
+
"bigbench/hhh_alignment",
|
640 |
+
"bigbench/formal_fallacies_syllogisms_negation",
|
641 |
+
"bigbench/bbq_lite_json",
|
642 |
+
"bigbench/cause_and_effect",
|
643 |
+
"bigbench/logic_grid_puzzle",
|
644 |
+
"bigbench/empirical_judgments",
|
645 |
+
"bigbench/human_organs_senses",
|
646 |
+
"bigbench/misconceptions",
|
647 |
+
"bigbench/strange_stories",
|
648 |
+
"bigbench/logical_args",
|
649 |
+
"bigbench/known_unknowns",
|
650 |
+
"bigbench/cs_algorithms",
|
651 |
+
"bigbench/emojis_emotion_prediction",
|
652 |
+
"bigbench/cifar10_classification",
|
653 |
+
"bigbench/penguins_in_a_table",
|
654 |
+
"bigbench/odd_one_out",
|
655 |
+
"bigbench/intent_recognition",
|
656 |
+
"bigbench/physics",
|
657 |
+
"bigbench/conceptual_combinations",
|
658 |
+
"bigbench/logical_deduction",
|
659 |
+
"bigbench/causal_judgment",
|
660 |
+
"bigbench/winowhy",
|
661 |
+
"bigbench/arithmetic",
|
662 |
+
"bigbench/undo_permutation",
|
663 |
+
"bigbench/analogical_similarity",
|
664 |
+
"bigbench/social_iqa",
|
665 |
+
"bigbench/key_value_maps",
|
666 |
+
"bigbench/implicatures",
|
667 |
+
"bigbench/real_or_fake_text",
|
668 |
+
"bigbench/disambiguation_qa",
|
669 |
+
"bigbench/similarities_abstraction",
|
670 |
+
"bigbench/movie_dialog_same_or_different",
|
671 |
+
"bigbench/english_proverbs",
|
672 |
+
"bigbench/presuppositions_as_nli",
|
673 |
+
"bigbench/entailed_polarity",
|
674 |
+
"bigbench/snarks",
|
675 |
+
"bigbench/goal_step_wikihow",
|
676 |
+
"bigbench/crass_ai",
|
677 |
+
"bigbench/play_dialog_same_or_different",
|
678 |
+
"bigbench/hindu_knowledge",
|
679 |
+
"bigbench/international_phonetic_alphabet_nli",
|
680 |
+
"bigbench/understanding_fables",
|
681 |
+
"bigbench/geometric_shapes",
|
682 |
+
"bigbench/code_line_description",
|
683 |
+
"bigbench/riddle_sense",
|
684 |
+
"bigbench/symbol_interpretation",
|
685 |
+
"bigbench/irony_identification",
|
686 |
+
"bigbench/anachronisms",
|
687 |
+
"bigbench/navigate",
|
688 |
+
"bigbench/crash_blossom",
|
689 |
+
"bigbench/identify_odd_metaphor",
|
690 |
+
"bigbench/simple_ethical_questions",
|
691 |
+
"bigbench/contextual_parametric_knowledge_conflicts",
|
692 |
+
"bigbench/date_understanding",
|
693 |
+
"bigbench/figure_of_speech_detection",
|
694 |
+
"bigbench/question_selection",
|
695 |
+
"bigbench/elementary_math_qa",
|
696 |
+
"bigbench/nonsense_words_grammar",
|
697 |
+
"bigbench/salient_translation_error_detection",
|
698 |
+
"bigbench/epistemic_reasoning",
|
699 |
+
"bigbench/movie_recommendation",
|
700 |
+
"bigbench/strategyqa",
|
701 |
+
"bigbench/tracking_shuffled_objects",
|
702 |
+
"bigbench/unit_interpretation",
|
703 |
+
"bigbench/reasoning_about_colored_objects",
|
704 |
+
"bigbench/discourse_marker_prediction",
|
705 |
+
"bigbench/logical_fallacy_detection",
|
706 |
+
"bigbench/general_knowledge",
|
707 |
+
"bigbench/abstract_narrative_understanding",
|
708 |
+
"bigbench/color",
|
709 |
+
"bigbench/hyperbaton",
|
710 |
+
"bigbench/logical_sequence",
|
711 |
+
"bigbench/mnist_ascii",
|
712 |
+
"bigbench/fantasy_reasoning",
|
713 |
+
"bigbench/mathematical_induction",
|
714 |
+
"bigbench/timedial",
|
715 |
+
"bigbench/identify_math_theorems",
|
716 |
+
"bigbench/checkmate_in_one",
|
717 |
+
"bigbench/phrase_relatedness",
|
718 |
+
"bigbench/ruin_names",
|
719 |
+
"bigbench/gre_reading_comprehension",
|
720 |
+
"bigbench/metaphor_boolean",
|
721 |
+
"bigbench/sentence_ambiguity",
|
722 |
+
"bigbench/vitaminc_fact_verification",
|
723 |
+
"bigbench/evaluating_information_essentiality",
|
724 |
+
"cos_e/v1.0",
|
725 |
+
"cosmos_qa",
|
726 |
+
"dream",
|
727 |
+
"openbookqa",
|
728 |
+
"qasc",
|
729 |
+
"quartz",
|
730 |
+
"quail",
|
731 |
+
"head_qa/en",
|
732 |
+
"sciq",
|
733 |
+
"social_i_qa",
|
734 |
+
"wiki_hop/original",
|
735 |
+
"wiqa",
|
736 |
+
"piqa",
|
737 |
+
"hellaswag",
|
738 |
+
"super_glue/copa",
|
739 |
+
"balanced-copa",
|
740 |
+
"e-CARE",
|
741 |
+
"art",
|
742 |
+
"winogrande/winogrande_xl",
|
743 |
+
"codah/codah",
|
744 |
+
"ai2_arc/ARC-Challenge/challenge",
|
745 |
+
"ai2_arc/ARC-Easy/challenge",
|
746 |
+
"definite_pronoun_resolution",
|
747 |
+
"swag/regular",
|
748 |
+
"math_qa",
|
749 |
+
"glue/cola",
|
750 |
+
"glue/sst2",
|
751 |
+
"utilitarianism",
|
752 |
+
"amazon_counterfactual/en",
|
753 |
+
"insincere-questions",
|
754 |
+
"toxic_conversations",
|
755 |
+
"TuringBench",
|
756 |
+
"trec",
|
757 |
+
"vitaminc/tals--vitaminc",
|
758 |
+
"hope_edi/english",
|
759 |
+
"rumoureval_2019/RumourEval2019",
|
760 |
+
"ethos/binary",
|
761 |
+
"ethos/multilabel",
|
762 |
+
"tweet_eval/sentiment",
|
763 |
+
"tweet_eval/irony",
|
764 |
+
"tweet_eval/offensive",
|
765 |
+
"tweet_eval/hate",
|
766 |
+
"tweet_eval/emotion",
|
767 |
+
"tweet_eval/emoji",
|
768 |
+
"tweet_eval/stance_abortion",
|
769 |
+
"tweet_eval/stance_atheism",
|
770 |
+
"tweet_eval/stance_climate",
|
771 |
+
"tweet_eval/stance_feminist",
|
772 |
+
"tweet_eval/stance_hillary",
|
773 |
+
"discovery/discovery",
|
774 |
+
"pragmeval/squinky-informativeness",
|
775 |
+
"pragmeval/emobank-arousal",
|
776 |
+
"pragmeval/switchboard",
|
777 |
+
"pragmeval/squinky-implicature",
|
778 |
+
"pragmeval/emobank-valence",
|
779 |
+
"pragmeval/mrda",
|
780 |
+
"pragmeval/squinky-formality",
|
781 |
+
"pragmeval/verifiability",
|
782 |
+
"pragmeval/emobank-dominance",
|
783 |
+
"pragmeval/persuasiveness-specificity",
|
784 |
+
"pragmeval/persuasiveness-strength",
|
785 |
+
"pragmeval/persuasiveness-claimtype",
|
786 |
+
"pragmeval/pdtb",
|
787 |
+
"pragmeval/sarcasm",
|
788 |
+
"pragmeval/stac",
|
789 |
+
"pragmeval/persuasiveness-premisetype",
|
790 |
+
"pragmeval/persuasiveness-eloquence",
|
791 |
+
"pragmeval/gum",
|
792 |
+
"pragmeval/emergent",
|
793 |
+
"pragmeval/persuasiveness-relevance",
|
794 |
+
"silicone/dyda_da",
|
795 |
+
"silicone/dyda_e",
|
796 |
+
"silicone/maptask",
|
797 |
+
"silicone/meld_e",
|
798 |
+
"silicone/meld_s",
|
799 |
+
"silicone/sem",
|
800 |
+
"silicone/oasis",
|
801 |
+
"silicone/iemocap",
|
802 |
+
"lex_glue/eurlex",
|
803 |
+
"lex_glue/scotus",
|
804 |
+
"lex_glue/ledgar",
|
805 |
+
"lex_glue/unfair_tos",
|
806 |
+
"lex_glue/case_hold",
|
807 |
+
"language-identification",
|
808 |
+
"imdb",
|
809 |
+
"rotten_tomatoes",
|
810 |
+
"ag_news",
|
811 |
+
"yelp_review_full/yelp_review_full",
|
812 |
+
"financial_phrasebank/sentences_allagree",
|
813 |
+
"poem_sentiment",
|
814 |
+
"dbpedia_14/dbpedia_14",
|
815 |
+
"amazon_polarity/amazon_polarity",
|
816 |
+
"app_reviews",
|
817 |
+
"hate_speech18",
|
818 |
+
"sms_spam",
|
819 |
+
"humicroedit/subtask-1",
|
820 |
+
"humicroedit/subtask-2",
|
821 |
+
"snips_built_in_intents",
|
822 |
+
"hate_speech_offensive",
|
823 |
+
"yahoo_answers_topics",
|
824 |
+
"stackoverflow-questions",
|
825 |
+
"hyperpartisan_news",
|
826 |
+
"sciie",
|
827 |
+
"citation_intent",
|
828 |
+
"go_emotions/simplified",
|
829 |
+
"scicite",
|
830 |
+
"liar",
|
831 |
+
"lexical_relation_classification/ROOT09",
|
832 |
+
"lexical_relation_classification/BLESS",
|
833 |
+
"lexical_relation_classification/CogALexV",
|
834 |
+
"lexical_relation_classification/EVALution",
|
835 |
+
"lexical_relation_classification/K&H+N",
|
836 |
+
"linguisticprobing/coordination_inversion",
|
837 |
+
"linguisticprobing/obj_number",
|
838 |
+
"linguisticprobing/past_present",
|
839 |
+
"linguisticprobing/sentence_length",
|
840 |
+
"linguisticprobing/subj_number",
|
841 |
+
"linguisticprobing/odd_man_out",
|
842 |
+
"linguisticprobing/tree_depth",
|
843 |
+
"linguisticprobing/top_constituents",
|
844 |
+
"linguisticprobing/bigram_shift",
|
845 |
+
"crowdflower/political-media-message",
|
846 |
+
"crowdflower/political-media-audience",
|
847 |
+
"crowdflower/economic-news",
|
848 |
+
"crowdflower/text_emotion",
|
849 |
+
"crowdflower/political-media-bias",
|
850 |
+
"crowdflower/airline-sentiment",
|
851 |
+
"crowdflower/tweet_global_warming",
|
852 |
+
"crowdflower/corporate-messaging",
|
853 |
+
"crowdflower/sentiment_nuclear_power",
|
854 |
+
"ethics/commonsense",
|
855 |
+
"ethics/deontology",
|
856 |
+
"ethics/justice",
|
857 |
+
"ethics/virtue",
|
858 |
+
"emo/emo2019",
|
859 |
+
"google_wellformed_query",
|
860 |
+
"tweets_hate_speech_detection",
|
861 |
+
"has_part",
|
862 |
+
"wnut_17/wnut_17",
|
863 |
+
"ncbi_disease/ncbi_disease",
|
864 |
+
"acronym_identification",
|
865 |
+
"jnlpba/jnlpba",
|
866 |
+
"ontonotes_english/SpeedOfMagic--ontonotes_english",
|
867 |
+
"blog_authorship_corpus/gender",
|
868 |
+
"blog_authorship_corpus/age",
|
869 |
+
"blog_authorship_corpus/job",
|
870 |
+
"open_question_type",
|
871 |
+
"health_fact",
|
872 |
+
"commonsense_qa",
|
873 |
+
"mc_taco",
|
874 |
+
"ade_corpus_v2/Ade_corpus_v2_classification",
|
875 |
+
"discosense",
|
876 |
+
"circa",
|
877 |
+
"phrase_similarity",
|
878 |
+
"scientific-exaggeration-detection",
|
879 |
+
"quarel",
|
880 |
+
"fever-evidence-related/mwong--fever-related",
|
881 |
+
"numer_sense",
|
882 |
+
"dynasent/dynabench.dynasent.r1.all/r1",
|
883 |
+
"dynasent/dynabench.dynasent.r2.all/r2",
|
884 |
+
"Sarcasm_News_Headline",
|
885 |
+
"sem_eval_2010_task_8",
|
886 |
+
"auditor_review/demo-org--auditor_review",
|
887 |
+
"medmcqa",
|
888 |
+
"Dynasent_Disagreement",
|
889 |
+
"Politeness_Disagreement",
|
890 |
+
"SBIC_Disagreement",
|
891 |
+
"SChem_Disagreement",
|
892 |
+
"Dilemmas_Disagreement",
|
893 |
+
"logiqa",
|
894 |
+
"wiki_qa",
|
895 |
+
"cycic_classification",
|
896 |
+
"cycic_multiplechoice",
|
897 |
+
"sts-companion",
|
898 |
+
"commonsense_qa_2.0",
|
899 |
+
"lingnli",
|
900 |
+
"monotonicity-entailment",
|
901 |
+
"arct",
|
902 |
+
"scinli",
|
903 |
+
"naturallogic",
|
904 |
+
"onestop_qa",
|
905 |
+
"moral_stories/full",
|
906 |
+
"prost",
|
907 |
+
"dynahate",
|
908 |
+
"syntactic-augmentation-nli",
|
909 |
+
"autotnli",
|
910 |
+
"CONDAQA",
|
911 |
+
"webgpt_comparisons",
|
912 |
+
"synthetic-instruct-gptj-pairwise",
|
913 |
+
"scruples",
|
914 |
+
"wouldyourather",
|
915 |
+
"attempto-nli",
|
916 |
+
"defeasible-nli/snli",
|
917 |
+
"defeasible-nli/atomic",
|
918 |
+
"help-nli",
|
919 |
+
"nli-veridicality-transitivity",
|
920 |
+
"natural-language-satisfiability",
|
921 |
+
"lonli",
|
922 |
+
"dadc-limit-nli",
|
923 |
+
"FLUTE",
|
924 |
+
"strategy-qa",
|
925 |
+
"summarize_from_feedback/comparisons",
|
926 |
+
"folio",
|
927 |
+
"tomi-nli",
|
928 |
+
"avicenna",
|
929 |
+
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