Upload DebertaV2ForSequenceClassification

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+  "share_att_key": true,
+  "tasks": [
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+    "glue/qnli",
+    "glue/rte",
+    "glue/wnli",
+    "glue/mrpc",
+    "glue/qqp",
+    "glue/stsb",
+    "super_glue/boolq",
+    "super_glue/cb",
+    "super_glue/multirc",
+    "super_glue/wic",
+    "super_glue/axg",
+    "anli/a1",
+    "anli/a2",
+    "anli/a3",
+    "sick/label",
+    "sick/relatedness",
+    "sick/entailment_AB",
+    "snli",
+    "scitail/snli_format",
+    "hans",
+    "WANLI",
+    "recast/recast_verbnet",
+    "recast/recast_kg_relations",
+    "recast/recast_ner",
+    "recast/recast_factuality",
+    "recast/recast_puns",
+    "recast/recast_megaveridicality",
+    "recast/recast_sentiment",
+    "recast/recast_verbcorner",
+    "probability_words_nli/usnli",
+    "probability_words_nli/reasoning_1hop",
+    "probability_words_nli/reasoning_2hop",
+    "nan-nli/joey234--nan-nli",
+    "nli_fever",
+    "breaking_nli",
+    "conj_nli",
+    "fracas",
+    "dialogue_nli",
+    "mpe",
+    "dnc",
+    "recast_white/fnplus",
+    "recast_white/sprl",
+    "recast_white/dpr",
+    "joci",
+    "robust_nli/IS_CS",
+    "robust_nli/LI_LI",
+    "robust_nli/ST_WO",
+    "robust_nli/PI_SP",
+    "robust_nli/PI_CD",
+    "robust_nli/ST_SE",
+    "robust_nli/ST_NE",
+    "robust_nli/ST_LM",
+    "robust_nli_is_sd",
+    "robust_nli_li_ts",
+    "add_one_rte",
+    "imppres/implicature_numerals_10_100/log",
+    "imppres/implicature_connectives/log",
+    "imppres/implicature_modals/log",
+    "imppres/implicature_gradable_verb/log",
+    "imppres/implicature_gradable_adjective/log",
+    "imppres/implicature_numerals_2_3/log",
+    "imppres/implicature_quantifiers/log",
+    "glue_diagnostics/diagnostics",
+    "hlgd",
+    "paws/labeled_final",
+    "paws/labeled_swap",
+    "medical_questions_pairs",
+    "conll2003/pos_tags",
+    "conll2003/chunk_tags",
+    "conll2003/ner_tags",
+    "hh-rlhf",
+    "model-written-evals",
+    "truthful_qa/multiple_choice",
+    "fig-qa",
+    "bigbench/physical_intuition",
+    "bigbench/authorship_verification",
+    "bigbench/implicit_relations",
+    "bigbench/dyck_languages",
+    "bigbench/novel_concepts",
+    "bigbench/moral_permissibility",
+    "bigbench/metaphor_understanding",
+    "bigbench/temporal_sequences",
+    "bigbench/sports_understanding",
+    "bigbench/analytic_entailment",
+    "bigbench/social_support",
+    "bigbench/emoji_movie",
+    "bigbench/dark_humor_detection",
+    "bigbench/suicide_risk",
+    "bigbench/fact_checker",
+    "bigbench/hhh_alignment",
+    "bigbench/formal_fallacies_syllogisms_negation",
+    "bigbench/bbq_lite_json",
+    "bigbench/cause_and_effect",
+    "bigbench/logic_grid_puzzle",
+    "bigbench/empirical_judgments",
+    "bigbench/human_organs_senses",
+    "bigbench/misconceptions",
+    "bigbench/strange_stories",
+    "bigbench/logical_args",
+    "bigbench/known_unknowns",
+    "bigbench/cs_algorithms",
+    "bigbench/emojis_emotion_prediction",
+    "bigbench/cifar10_classification",
+    "bigbench/penguins_in_a_table",
+    "bigbench/odd_one_out",
+    "bigbench/intent_recognition",
+    "bigbench/physics",
+    "bigbench/conceptual_combinations",
+    "bigbench/logical_deduction",
+    "bigbench/causal_judgment",
+    "bigbench/winowhy",
+    "bigbench/arithmetic",
+    "bigbench/undo_permutation",
+    "bigbench/analogical_similarity",
+    "bigbench/social_iqa",
+    "bigbench/key_value_maps",
+    "bigbench/implicatures",
+    "bigbench/real_or_fake_text",
+    "bigbench/disambiguation_qa",
+    "bigbench/similarities_abstraction",
+    "bigbench/movie_dialog_same_or_different",
+    "bigbench/english_proverbs",
+    "bigbench/presuppositions_as_nli",
+    "bigbench/entailed_polarity",
+    "bigbench/snarks",
+    "bigbench/goal_step_wikihow",
+    "bigbench/crass_ai",
+    "bigbench/play_dialog_same_or_different",
+    "bigbench/hindu_knowledge",
+    "bigbench/international_phonetic_alphabet_nli",
+    "bigbench/understanding_fables",
+    "bigbench/geometric_shapes",
+    "bigbench/code_line_description",
+    "bigbench/riddle_sense",
+    "bigbench/symbol_interpretation",
+    "bigbench/irony_identification",
+    "bigbench/anachronisms",
+    "bigbench/navigate",
+    "bigbench/crash_blossom",
+    "bigbench/identify_odd_metaphor",
+    "bigbench/simple_ethical_questions",
+    "bigbench/contextual_parametric_knowledge_conflicts",
+    "bigbench/date_understanding",
+    "bigbench/figure_of_speech_detection",
+    "bigbench/question_selection",
+    "bigbench/elementary_math_qa",
+    "bigbench/nonsense_words_grammar",
+    "bigbench/salient_translation_error_detection",
+    "bigbench/epistemic_reasoning",
+    "bigbench/movie_recommendation",
+    "bigbench/strategyqa",
+    "bigbench/tracking_shuffled_objects",
+    "bigbench/unit_interpretation",
+    "bigbench/reasoning_about_colored_objects",
+    "bigbench/discourse_marker_prediction",
+    "bigbench/logical_fallacy_detection",
+    "bigbench/general_knowledge",
+    "bigbench/abstract_narrative_understanding",
+    "bigbench/color",
+    "bigbench/hyperbaton",
+    "bigbench/logical_sequence",
+    "bigbench/mnist_ascii",
+    "bigbench/fantasy_reasoning",
+    "bigbench/mathematical_induction",
+    "bigbench/timedial",
+    "bigbench/identify_math_theorems",
+    "bigbench/checkmate_in_one",
+    "bigbench/phrase_relatedness",
+    "bigbench/ruin_names",
+    "bigbench/gre_reading_comprehension",
+    "bigbench/metaphor_boolean",
+    "bigbench/sentence_ambiguity",
+    "bigbench/vitaminc_fact_verification",
+    "bigbench/evaluating_information_essentiality",
+    "cos_e/v1.0",
+    "cosmos_qa",
+    "dream",
+    "openbookqa",
+    "qasc",
+    "quartz",
+    "quail",
+    "head_qa/en",
+    "sciq",
+    "social_i_qa",
+    "wiki_hop/original",
+    "wiqa",
+    "piqa",
+    "hellaswag",
+    "super_glue/copa",
+    "balanced-copa",
+    "e-CARE",
+    "art",
+    "winogrande/winogrande_xl",
+    "codah/codah",
+    "ai2_arc/ARC-Challenge/challenge",
+    "ai2_arc/ARC-Easy/challenge",
+    "definite_pronoun_resolution",
+    "swag/regular",
+    "math_qa",
+    "glue/cola",
+    "glue/sst2",
+    "utilitarianism",
+    "amazon_counterfactual/en",
+    "insincere-questions",
+    "toxic_conversations",
+    "TuringBench",
+    "trec",
+    "vitaminc/tals--vitaminc",
+    "hope_edi/english",
+    "rumoureval_2019/RumourEval2019",
+    "ethos/binary",
+    "ethos/multilabel",
+    "tweet_eval/sentiment",
+    "tweet_eval/irony",
+    "tweet_eval/offensive",
+    "tweet_eval/hate",
+    "tweet_eval/emotion",
+    "tweet_eval/emoji",
+    "tweet_eval/stance_abortion",
+    "tweet_eval/stance_atheism",
+    "tweet_eval/stance_climate",
+    "tweet_eval/stance_feminist",
+    "tweet_eval/stance_hillary",
+    "discovery/discovery",
+    "pragmeval/squinky-informativeness",
+    "pragmeval/emobank-arousal",
+    "pragmeval/switchboard",
+    "pragmeval/squinky-implicature",
+    "pragmeval/emobank-valence",
+    "pragmeval/mrda",
+    "pragmeval/squinky-formality",
+    "pragmeval/verifiability",
+    "pragmeval/emobank-dominance",
+    "pragmeval/persuasiveness-specificity",
+    "pragmeval/persuasiveness-strength",
+    "pragmeval/persuasiveness-claimtype",
+    "pragmeval/pdtb",
+    "pragmeval/sarcasm",
+    "pragmeval/stac",
+    "pragmeval/persuasiveness-premisetype",
+    "pragmeval/persuasiveness-eloquence",
+    "pragmeval/gum",
+    "pragmeval/emergent",
+    "pragmeval/persuasiveness-relevance",
+    "silicone/dyda_da",
+    "silicone/dyda_e",
+    "silicone/maptask",
+    "silicone/meld_e",
+    "silicone/meld_s",
+    "silicone/sem",
+    "silicone/oasis",
+    "silicone/iemocap",
+    "lex_glue/eurlex",
+    "lex_glue/scotus",
+    "lex_glue/ledgar",
+    "lex_glue/unfair_tos",
+    "lex_glue/case_hold",
+    "language-identification",
+    "imdb",
+    "rotten_tomatoes",
+    "ag_news",
+    "yelp_review_full/yelp_review_full",
+    "financial_phrasebank/sentences_allagree",
+    "poem_sentiment",
+    "dbpedia_14/dbpedia_14",
+    "amazon_polarity/amazon_polarity",
+    "app_reviews",
+    "hate_speech18",
+    "sms_spam",
+    "humicroedit/subtask-1",
+    "humicroedit/subtask-2",
+    "snips_built_in_intents",
+    "hate_speech_offensive",
+    "yahoo_answers_topics",
+    "stackoverflow-questions",
+    "hyperpartisan_news",
+    "sciie",
+    "citation_intent",
+    "go_emotions/simplified",
+    "scicite",
+    "liar",
+    "lexical_relation_classification/ROOT09",
+    "lexical_relation_classification/BLESS",
+    "lexical_relation_classification/CogALexV",
+    "lexical_relation_classification/EVALution",
+    "lexical_relation_classification/K&H+N",
+    "linguisticprobing/coordination_inversion",
+    "linguisticprobing/obj_number",
+    "linguisticprobing/past_present",
+    "linguisticprobing/sentence_length",
+    "linguisticprobing/subj_number",
+    "linguisticprobing/odd_man_out",
+    "linguisticprobing/tree_depth",
+    "linguisticprobing/top_constituents",
+    "linguisticprobing/bigram_shift",
+    "crowdflower/political-media-message",
+    "crowdflower/political-media-audience",
+    "crowdflower/economic-news",
+    "crowdflower/text_emotion",
+    "crowdflower/political-media-bias",
+    "crowdflower/airline-sentiment",
+    "crowdflower/tweet_global_warming",
+    "crowdflower/corporate-messaging",
+    "crowdflower/sentiment_nuclear_power",
+    "ethics/commonsense",
+    "ethics/deontology",
+    "ethics/justice",
+    "ethics/virtue",
+    "emo/emo2019",
+    "google_wellformed_query",
+    "tweets_hate_speech_detection",
+    "has_part",
+    "wnut_17/wnut_17",
+    "ncbi_disease/ncbi_disease",
+    "acronym_identification",
+    "jnlpba/jnlpba",
+    "ontonotes_english/SpeedOfMagic--ontonotes_english",
+    "blog_authorship_corpus/gender",
+    "blog_authorship_corpus/age",
+    "blog_authorship_corpus/job",
+    "open_question_type",
+    "health_fact",
+    "commonsense_qa",
+    "mc_taco",
+    "ade_corpus_v2/Ade_corpus_v2_classification",
+    "discosense",
+    "circa",
+    "phrase_similarity",
+    "scientific-exaggeration-detection",
+    "quarel",
+    "fever-evidence-related/mwong--fever-related",
+    "numer_sense",
+    "dynasent/dynabench.dynasent.r1.all/r1",
+    "dynasent/dynabench.dynasent.r2.all/r2",
+    "Sarcasm_News_Headline",
+    "sem_eval_2010_task_8",
+    "auditor_review/demo-org--auditor_review",
+    "medmcqa",
+    "Dynasent_Disagreement",
+    "Politeness_Disagreement",
+    "SBIC_Disagreement",
+    "SChem_Disagreement",
+    "Dilemmas_Disagreement",
+    "logiqa",
+    "wiki_qa",
+    "cycic_classification",
+    "cycic_multiplechoice",
+    "sts-companion",
+    "commonsense_qa_2.0",
+    "lingnli",
+    "monotonicity-entailment",
+    "arct",
+    "scinli",
+    "naturallogic",
+    "onestop_qa",
+    "moral_stories/full",
+    "prost",
+    "dynahate",
+    "syntactic-augmentation-nli",
+    "autotnli",
+    "CONDAQA",
+    "webgpt_comparisons",
+    "synthetic-instruct-gptj-pairwise",
+    "scruples",
+    "wouldyourather",
+    "attempto-nli",
+    "defeasible-nli/snli",
+    "defeasible-nli/atomic",
+    "help-nli",
+    "nli-veridicality-transitivity",
+    "natural-language-satisfiability",
+    "lonli",
+    "dadc-limit-nli",
+    "FLUTE",
+    "strategy-qa",
+    "summarize_from_feedback/comparisons",
+    "folio",
+    "tomi-nli",
+    "avicenna",
+    "SHP",
+    "MedQA-USMLE-4-options-hf",
+    "wikimedqa/medwiki",
+    "cicero",
+    "CREAK",
+    "mutual",
+    "NeQA",
+    "quote-repetition",
+    "redefine-math",
+    "puzzte",
+    "implicatures",
+    "race/high",
+    "race/middle",
+    "race-c",
+    "spartqa-yn",
+    "spartqa-mchoice",
+    "temporal-nli",
+    "riddle_sense",
+    "clcd-english",
+    "twentyquestions",
+    "reclor",
+    "counterfactually-augmented-imdb",
+    "counterfactually-augmented-snli",
+    "cnli",
+    "boolq-natural-perturbations",
+    "acceptability-prediction",
+    "equate",
+    "ScienceQA_text_only",
+    "ekar_english",
+    "implicit-hate-stg1",
+    "chaos-mnli-ambiguity",
+    "headline_cause/en_simple",
+    "logiqa-2.0-nli",
+    "oasst1_dense_flat/quality",
+    "oasst1_dense_flat/toxicity",
+    "oasst1_dense_flat/helpfulness",
+    "PARARULE-Plus",
+    "mindgames",
+    "universal_dependencies/en_lines/deprel",
+    "universal_dependencies/en_partut/deprel",
+    "universal_dependencies/en_ewt/deprel",
+    "universal_dependencies/en_gum/deprel",
+    "ambient",
+    "path-naturalness-prediction",
+    "civil_comments/toxicity",
+    "civil_comments/severe_toxicity",
+    "civil_comments/obscene",
+    "civil_comments/threat",
+    "civil_comments/insult",
+    "civil_comments/identity_attack",
+    "civil_comments/sexual_explicit",
+    "cloth",
+    "dgen",
+    "oasst1_pairwise_rlhf_reward",
+    "I2D2",
+    "args_me",
+    "Touche23-ValueEval",
+    "starcon",
+    "banking77",
+    "ruletaker",
+    "lsat_qa/all",
+    "ConTRoL-nli",
+    "tracie",
+    "sherliic",
+    "sen-making/1",
+    "sen-making/2",
+    "winowhy",
+    "mbib-base/cognitive-bias",
+    "mbib-base/fake-news",
+    "mbib-base/gender-bias",
+    "mbib-base/hate-speech",
+    "mbib-base/linguistic-bias",
+    "mbib-base/political-bias",
+    "mbib-base/racial-bias",
+    "mbib-base/text-level-bias",
+    "robustLR",
+    "v1/gen_train234_test2to10",
+    "logical-fallacy",
+    "parade",
+    "cladder",
+    "subjectivity",
+    "MOH",
+    "VUAC",
+    "TroFi",
+    "sharc_modified/mod",
+    "conceptrules_v2",
+    "disrpt/eng.dep.scidtb.rels",
+    "conll2000",
+    "few-nerd/supervised",
+    "finer-139",
+    "zero-shot-label-nli",
+    "com2sense",
+    "scone",
+    "winodict",
+    "fool-me-twice",
+    "monli",
+    "corr2cause",
+    "lsat_qa/all",
+    "apt",
+    "twitter-financial-news-sentiment",
+    "icl-symbol-tuning-instruct",
+    "SpaceNLI",
+    "propsegment/nli",
+    "HatemojiBuild",
+    "regset",
+    "esci",
+    "chatbot_arena_conversations",
+    "dnd_style_intents",
+    "FLD.v2",
+    "SDOH-NLI",
+    "scifact_entailment",
+    "feasibilityQA",
+    "simple_pair",
+    "AdjectiveScaleProbe-nli",
+    "resnli",
+    "SpaRTUN",
+    "ReSQ",
+    "semantic_fragments_nli",
+    "dataset_train_nli",
+    "babi_nli",
+    "gen_debiased_nli",
+    "imppres/presupposition",
+    "/prag",
+    "blimp-2",
+    "mmlu-4"
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.3",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

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