Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
text-classification
deberta-v3-base
deberta-v3
deberta
nli
natural-language-inference
multitask
multi-task
pipeline
extreme-multi-task
extreme-mtl
tasksource
zero-shot
rlhf
Eval Results (legacy)
Instructions to use sileod/deberta-v3-base-tasksource-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sileod/deberta-v3-base-tasksource-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="sileod/deberta-v3-base-tasksource-nli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sileod/deberta-v3-base-tasksource-nli") model = AutoModelForSequenceClassification.from_pretrained("sileod/deberta-v3-base-tasksource-nli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Upload DebertaV2ForSequenceClassification
Browse files- config.json +148 -146
- pytorch_model.bin +1 -1
config.json
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| 257 |
+
42,
|
| 258 |
3,
|
|
|
|
| 259 |
12,
|
| 260 |
11,
|
|
|
|
| 261 |
100,
|
| 262 |
13,
|
| 263 |
100,
|
|
|
|
| 287 |
28,
|
| 288 |
3,
|
| 289 |
6,
|
| 290 |
+
5,
|
| 291 |
7,
|
|
|
|
| 292 |
3,
|
| 293 |
+
6,
|
| 294 |
+
4,
|
|
|
|
| 295 |
2,
|
|
|
|
| 296 |
20,
|
| 297 |
6,
|
| 298 |
2,
|
| 299 |
2,
|
| 300 |
2,
|
|
|
|
| 301 |
2,
|
| 302 |
+
2,
|
| 303 |
+
7,
|
| 304 |
+
2,
|
| 305 |
2,
|
| 306 |
3,
|
| 307 |
4,
|
| 308 |
+
9,
|
| 309 |
2,
|
| 310 |
+
4,
|
| 311 |
+
3,
|
| 312 |
+
13,
|
| 313 |
2,
|
| 314 |
2,
|
| 315 |
2,
|
|
|
|
| 488 |
2,
|
| 489 |
3,
|
| 490 |
2,
|
| 491 |
+
3,
|
| 492 |
2,
|
| 493 |
3,
|
| 494 |
3,
|
|
|
|
| 553 |
"scitail/snli_format",
|
| 554 |
"hans",
|
| 555 |
"WANLI",
|
|
|
|
| 556 |
"recast/recast_ner",
|
| 557 |
+
"recast/recast_sentiment",
|
| 558 |
+
"recast/recast_megaveridicality",
|
|
|
|
| 559 |
"recast/recast_verbnet",
|
| 560 |
+
"recast/recast_puns",
|
| 561 |
+
"recast/recast_kg_relations",
|
| 562 |
"recast/recast_verbcorner",
|
| 563 |
+
"recast/recast_factuality",
|
| 564 |
+
"probability_words_nli/usnli",
|
| 565 |
"probability_words_nli/reasoning_2hop",
|
| 566 |
"probability_words_nli/reasoning_1hop",
|
|
|
|
| 567 |
"nan-nli/joey234--nan-nli",
|
| 568 |
"nli_fever",
|
| 569 |
"breaking_nli",
|
|
|
|
| 587 |
"robust_nli_is_sd",
|
| 588 |
"robust_nli_li_ts",
|
| 589 |
"add_one_rte",
|
| 590 |
+
"imppres/implicature_gradable_verb/log",
|
|
|
|
| 591 |
"imppres/implicature_quantifiers/log",
|
| 592 |
+
"imppres/implicature_connectives/log",
|
| 593 |
"imppres/implicature_numerals_2_3/log",
|
| 594 |
"imppres/implicature_numerals_10_100/log",
|
| 595 |
+
"imppres/implicature_gradable_adjective/log",
|
| 596 |
+
"imppres/implicature_modals/log",
|
| 597 |
"glue_diagnostics/diagnostics",
|
| 598 |
"hlgd",
|
| 599 |
"paws/labeled_final",
|
|
|
|
| 606 |
"model-written-evals",
|
| 607 |
"truthful_qa/multiple_choice",
|
| 608 |
"fig-qa",
|
| 609 |
+
"bigbench/sports_understanding",
|
| 610 |
+
"bigbench/analytic_entailment",
|
| 611 |
+
"bigbench/presuppositions_as_nli",
|
| 612 |
+
"bigbench/logic_grid_puzzle",
|
| 613 |
"bigbench/geometric_shapes",
|
| 614 |
+
"bigbench/metaphor_understanding",
|
| 615 |
+
"bigbench/movie_recommendation",
|
| 616 |
+
"bigbench/gre_reading_comprehension",
|
| 617 |
"bigbench/logical_args",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 618 |
"bigbench/similarities_abstraction",
|
| 619 |
+
"bigbench/nonsense_words_grammar",
|
| 620 |
"bigbench/misconceptions",
|
| 621 |
+
"bigbench/winowhy",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 622 |
"bigbench/understanding_fables",
|
| 623 |
+
"bigbench/logical_sequence",
|
| 624 |
+
"bigbench/hyperbaton",
|
| 625 |
"bigbench/emojis_emotion_prediction",
|
| 626 |
+
"bigbench/discourse_marker_prediction",
|
| 627 |
+
"bigbench/cause_and_effect",
|
| 628 |
+
"bigbench/crass_ai",
|
| 629 |
+
"bigbench/real_or_fake_text",
|
| 630 |
+
"bigbench/date_understanding",
|
| 631 |
+
"bigbench/analogical_similarity",
|
| 632 |
+
"bigbench/crash_blossom",
|
| 633 |
+
"bigbench/undo_permutation",
|
| 634 |
+
"bigbench/english_proverbs",
|
| 635 |
+
"bigbench/logical_fallacy_detection",
|
| 636 |
+
"bigbench/question_selection",
|
| 637 |
+
"bigbench/human_organs_senses",
|
| 638 |
+
"bigbench/cifar10_classification",
|
| 639 |
+
"bigbench/social_iqa",
|
| 640 |
+
"bigbench/abstract_narrative_understanding",
|
| 641 |
+
"bigbench/emoji_movie",
|
| 642 |
+
"bigbench/sentence_ambiguity",
|
| 643 |
+
"bigbench/elementary_math_qa",
|
| 644 |
"bigbench/epistemic_reasoning",
|
| 645 |
+
"bigbench/metaphor_boolean",
|
| 646 |
+
"bigbench/tracking_shuffled_objects",
|
| 647 |
+
"bigbench/physical_intuition",
|
| 648 |
+
"bigbench/key_value_maps",
|
| 649 |
+
"bigbench/penguins_in_a_table",
|
| 650 |
+
"bigbench/formal_fallacies_syllogisms_negation",
|
| 651 |
+
"bigbench/riddle_sense",
|
| 652 |
+
"bigbench/logical_deduction",
|
| 653 |
+
"bigbench/entailed_polarity",
|
| 654 |
+
"bigbench/ruin_names",
|
| 655 |
+
"bigbench/timedial",
|
| 656 |
+
"bigbench/fantasy_reasoning",
|
| 657 |
+
"bigbench/causal_judgment",
|
| 658 |
+
"bigbench/social_support",
|
| 659 |
+
"bigbench/disambiguation_qa",
|
| 660 |
+
"bigbench/hindu_knowledge",
|
| 661 |
+
"bigbench/play_dialog_same_or_different",
|
| 662 |
+
"bigbench/intent_recognition",
|
| 663 |
+
"bigbench/code_line_description",
|
| 664 |
+
"bigbench/implicit_relations",
|
| 665 |
+
"bigbench/checkmate_in_one",
|
| 666 |
+
"bigbench/dark_humor_detection",
|
| 667 |
"bigbench/mathematical_induction",
|
| 668 |
+
"bigbench/identify_odd_metaphor",
|
| 669 |
+
"bigbench/implicatures",
|
| 670 |
+
"bigbench/irony_identification",
|
| 671 |
+
"bigbench/phrase_relatedness",
|
| 672 |
+
"bigbench/figure_of_speech_detection",
|
| 673 |
+
"bigbench/empirical_judgments",
|
| 674 |
+
"bigbench/identify_math_theorems",
|
| 675 |
+
"bigbench/evaluating_information_essentiality",
|
| 676 |
"bigbench/hhh_alignment",
|
| 677 |
+
"bigbench/reasoning_about_colored_objects",
|
| 678 |
+
"bigbench/goal_step_wikihow",
|
| 679 |
+
"bigbench/odd_one_out",
|
| 680 |
+
"bigbench/strategyqa",
|
| 681 |
+
"bigbench/international_phonetic_alphabet_nli",
|
| 682 |
+
"bigbench/known_unknowns",
|
| 683 |
+
"bigbench/anachronisms",
|
| 684 |
+
"bigbench/bbq_lite_json",
|
| 685 |
+
"bigbench/mnist_ascii",
|
| 686 |
"bigbench/novel_concepts",
|
| 687 |
"bigbench/authorship_verification",
|
|
|
|
| 688 |
"bigbench/physics",
|
| 689 |
+
"bigbench/vitaminc_fact_verification",
|
| 690 |
+
"bigbench/general_knowledge",
|
| 691 |
+
"bigbench/simple_ethical_questions",
|
|
|
|
|
|
|
|
|
|
|
|
|
| 692 |
"bigbench/snarks",
|
| 693 |
+
"bigbench/symbol_interpretation",
|
| 694 |
+
"bigbench/moral_permissibility",
|
| 695 |
"bigbench/arithmetic",
|
| 696 |
+
"bigbench/navigate",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 697 |
"bigbench/cs_algorithms",
|
| 698 |
+
"bigbench/temporal_sequences",
|
| 699 |
+
"bigbench/unit_interpretation",
|
|
|
|
|
|
|
|
|
|
|
|
|
| 700 |
"bigbench/movie_dialog_same_or_different",
|
| 701 |
+
"bigbench/fact_checker",
|
| 702 |
+
"bigbench/suicide_risk",
|
| 703 |
+
"bigbench/conceptual_combinations",
|
| 704 |
"bigbench/strange_stories",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 705 |
"bigbench/dyck_languages",
|
| 706 |
+
"bigbench/contextual_parametric_knowledge_conflicts",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 707 |
"bigbench/salient_translation_error_detection",
|
| 708 |
+
"bigbench/color",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 709 |
"cos_e/v1.0",
|
| 710 |
"cosmos_qa",
|
| 711 |
"dream",
|
|
|
|
| 745 |
"ethos/binary",
|
| 746 |
"ethos/multilabel",
|
| 747 |
"tweet_eval/hate",
|
| 748 |
+
"tweet_eval/emotion",
|
| 749 |
"tweet_eval/irony",
|
|
|
|
|
|
|
| 750 |
"tweet_eval/offensive",
|
| 751 |
+
"tweet_eval/sentiment",
|
| 752 |
+
"tweet_eval/stance_abortion",
|
| 753 |
"tweet_eval/stance_atheism",
|
| 754 |
+
"tweet_eval/stance_climate",
|
| 755 |
+
"tweet_eval/stance_feminist",
|
| 756 |
"tweet_eval/stance_hillary",
|
| 757 |
"tweet_eval/emoji",
|
|
|
|
| 758 |
"discovery/discovery",
|
|
|
|
|
|
|
|
|
|
| 759 |
"pragmeval/squinky-formality",
|
| 760 |
+
"pragmeval/emobank-valence",
|
| 761 |
+
"pragmeval/emobank-dominance",
|
| 762 |
"pragmeval/squinky-implicature",
|
| 763 |
"pragmeval/squinky-informativeness",
|
| 764 |
"pragmeval/switchboard",
|
| 765 |
"pragmeval/mrda",
|
| 766 |
"pragmeval/verifiability",
|
| 767 |
+
"pragmeval/emobank-arousal",
|
| 768 |
"pragmeval/emergent",
|
| 769 |
+
"pragmeval/stac",
|
| 770 |
"pragmeval/gum",
|
| 771 |
+
"pragmeval/persuasiveness-claimtype",
|
| 772 |
"pragmeval/persuasiveness-eloquence",
|
| 773 |
+
"pragmeval/pdtb",
|
| 774 |
"pragmeval/persuasiveness-relevance",
|
|
|
|
|
|
|
| 775 |
"pragmeval/sarcasm",
|
| 776 |
+
"pragmeval/persuasiveness-premisetype",
|
| 777 |
+
"pragmeval/persuasiveness-strength",
|
| 778 |
+
"pragmeval/persuasiveness-specificity",
|
| 779 |
"silicone/dyda_e",
|
| 780 |
+
"silicone/dyda_da",
|
|
|
|
| 781 |
"silicone/meld_e",
|
| 782 |
+
"silicone/meld_s",
|
| 783 |
+
"silicone/oasis",
|
| 784 |
+
"silicone/sem",
|
| 785 |
"silicone/maptask",
|
| 786 |
"silicone/iemocap",
|
|
|
|
| 787 |
"lex_glue/eurlex",
|
| 788 |
"lex_glue/scotus",
|
| 789 |
"lex_glue/ledgar",
|
|
|
|
| 813 |
"go_emotions/simplified",
|
| 814 |
"scicite",
|
| 815 |
"liar",
|
| 816 |
+
"lexical_relation_classification/CogALexV",
|
| 817 |
"lexical_relation_classification/EVALution",
|
|
|
|
| 818 |
"lexical_relation_classification/ROOT09",
|
| 819 |
+
"lexical_relation_classification/BLESS",
|
| 820 |
+
"lexical_relation_classification/K&H+N",
|
| 821 |
"linguisticprobing/bigram_shift",
|
|
|
|
|
|
|
|
|
|
| 822 |
"linguisticprobing/top_constituents",
|
| 823 |
"linguisticprobing/sentence_length",
|
|
|
|
|
|
|
| 824 |
"linguisticprobing/subj_number",
|
| 825 |
+
"linguisticprobing/obj_number",
|
| 826 |
+
"linguisticprobing/past_present",
|
| 827 |
+
"linguisticprobing/odd_man_out",
|
| 828 |
+
"linguisticprobing/coordination_inversion",
|
| 829 |
+
"linguisticprobing/tree_depth",
|
| 830 |
+
"crowdflower/tweet_global_warming",
|
| 831 |
"crowdflower/political-media-audience",
|
| 832 |
"crowdflower/economic-news",
|
| 833 |
+
"crowdflower/sentiment_nuclear_power",
|
| 834 |
+
"crowdflower/political-media-message",
|
| 835 |
+
"crowdflower/political-media-bias",
|
| 836 |
"crowdflower/corporate-messaging",
|
| 837 |
"crowdflower/airline-sentiment",
|
| 838 |
+
"crowdflower/text_emotion",
|
| 839 |
"ethics/commonsense",
|
| 840 |
"ethics/deontology",
|
| 841 |
"ethics/justice",
|
|
|
|
| 951 |
"oasst1_dense_flat/helpfulness",
|
| 952 |
"PARARULE-Plus",
|
| 953 |
"mindgames",
|
|
|
|
|
|
|
|
|
|
| 954 |
"universal_dependencies/en_ewt/deprel",
|
| 955 |
+
"universal_dependencies/en_gum/deprel",
|
| 956 |
+
"universal_dependencies/en_lines/deprel",
|
| 957 |
+
"universal_dependencies/en_partut/deprel",
|
| 958 |
"ambient",
|
| 959 |
"path-naturalness-prediction",
|
| 960 |
"civil_comments/toxicity",
|
|
|
|
| 1014 |
"apt",
|
| 1015 |
"twitter-financial-news-sentiment",
|
| 1016 |
"icl-symbol-tuning-instruct",
|
| 1017 |
+
"SpaceNLI",
|
| 1018 |
"babi_nli",
|
| 1019 |
"gen_debiased_nli",
|
| 1020 |
"imppres/presupposition",
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 737771833
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
+
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size 737771833
|