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README.md

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+---
+base_model: microsoft/deberta-v3-base
+datasets:
+- nyu-mll/glue
+- aps/super_glue
+- facebook/anli
+- tasksource/babi_nli
+- sick
+- snli
+- scitail
+- hans
+- alisawuffles/WANLI
+- tasksource/recast
+- sileod/probability_words_nli
+- joey234/nan-nli
+- pietrolesci/nli_fever
+- pietrolesci/breaking_nli
+- pietrolesci/conj_nli
+- pietrolesci/fracas
+- pietrolesci/dialogue_nli
+- pietrolesci/mpe
+- pietrolesci/dnc
+- pietrolesci/recast_white
+- pietrolesci/joci
+- pietrolesci/robust_nli
+- pietrolesci/robust_nli_is_sd
+- pietrolesci/robust_nli_li_ts
+- pietrolesci/gen_debiased_nli
+- pietrolesci/add_one_rte
+- tasksource/imppres
+- hlgd
+- paws
+- medical_questions_pairs
+- Anthropic/model-written-evals
+- truthful_qa
+- nightingal3/fig-qa
+- tasksource/bigbench
+- blimp
+- cos_e
+- cosmos_qa
+- dream
+- openbookqa
+- qasc
+- quartz
+- quail
+- head_qa
+- sciq
+- social_i_qa
+- wiki_hop
+- wiqa
+- piqa
+- hellaswag
+- pkavumba/balanced-copa
+- 12ml/e-CARE
+- art
+- winogrande
+- codah
+- ai2_arc
+- definite_pronoun_resolution
+- swag
+- math_qa
+- metaeval/utilitarianism
+- mteb/amazon_counterfactual
+- SetFit/insincere-questions
+- SetFit/toxic_conversations
+- turingbench/TuringBench
+- trec
+- tals/vitaminc
+- hope_edi
+- strombergnlp/rumoureval_2019
+- ethos
+- tweet_eval
+- discovery
+- pragmeval
+- silicone
+- lex_glue
+- papluca/language-identification
+- imdb
+- rotten_tomatoes
+- ag_news
+- yelp_review_full
+- financial_phrasebank
+- poem_sentiment
+- dbpedia_14
+- amazon_polarity
+- app_reviews
+- hate_speech18
+- sms_spam
+- humicroedit
+- snips_built_in_intents
+- hate_speech_offensive
+- yahoo_answers_topics
+- pacovaldez/stackoverflow-questions
+- zapsdcn/hyperpartisan_news
+- zapsdcn/sciie
+- zapsdcn/citation_intent
+- go_emotions
+- allenai/scicite
+- liar
+- relbert/lexical_relation_classification
+- tasksource/linguisticprobing
+- tasksource/crowdflower
+- metaeval/ethics
+- emo
+- google_wellformed_query
+- tweets_hate_speech_detection
+- has_part
+- blog_authorship_corpus
+- launch/open_question_type
+- health_fact
+- commonsense_qa
+- mc_taco
+- ade_corpus_v2
+- prajjwal1/discosense
+- circa
+- PiC/phrase_similarity
+- copenlu/scientific-exaggeration-detection
+- quarel
+- mwong/fever-evidence-related
+- numer_sense
+- dynabench/dynasent
+- raquiba/Sarcasm_News_Headline
+- sem_eval_2010_task_8
+- demo-org/auditor_review
+- medmcqa
+- RuyuanWan/Dynasent_Disagreement
+- RuyuanWan/Politeness_Disagreement
+- RuyuanWan/SBIC_Disagreement
+- RuyuanWan/SChem_Disagreement
+- RuyuanWan/Dilemmas_Disagreement
+- lucasmccabe/logiqa
+- wiki_qa
+- tasksource/cycic_classification
+- tasksource/cycic_multiplechoice
+- tasksource/sts-companion
+- tasksource/commonsense_qa_2.0
+- tasksource/lingnli
+- tasksource/monotonicity-entailment
+- tasksource/arct
+- tasksource/scinli
+- tasksource/naturallogic
+- onestop_qa
+- demelin/moral_stories
+- corypaik/prost
+- aps/dynahate
+- metaeval/syntactic-augmentation-nli
+- tasksource/autotnli
+- lasha-nlp/CONDAQA
+- openai/webgpt_comparisons
+- Dahoas/synthetic-instruct-gptj-pairwise
+- metaeval/scruples
+- metaeval/wouldyourather
+- metaeval/defeasible-nli
+- tasksource/help-nli
+- metaeval/nli-veridicality-transitivity
+- tasksource/lonli
+- tasksource/dadc-limit-nli
+- ColumbiaNLP/FLUTE
+- tasksource/strategy-qa
+- openai/summarize_from_feedback
+- tasksource/folio
+- yale-nlp/FOLIO
+- tasksource/tomi-nli
+- tasksource/avicenna
+- stanfordnlp/SHP
+- GBaker/MedQA-USMLE-4-options-hf
+- sileod/wikimedqa
+- declare-lab/cicero
+- amydeng2000/CREAK
+- tasksource/mutual
+- inverse-scaling/NeQA
+- inverse-scaling/quote-repetition
+- inverse-scaling/redefine-math
+- tasksource/puzzte
+- tasksource/implicatures
+- race
+- tasksource/race-c
+- tasksource/spartqa-yn
+- tasksource/spartqa-mchoice
+- tasksource/temporal-nli
+- riddle_sense
+- tasksource/clcd-english
+- maximedb/twentyquestions
+- metaeval/reclor
+- tasksource/counterfactually-augmented-imdb
+- tasksource/counterfactually-augmented-snli
+- metaeval/cnli
+- tasksource/boolq-natural-perturbations
+- metaeval/acceptability-prediction
+- metaeval/equate
+- tasksource/ScienceQA_text_only
+- Jiangjie/ekar_english
+- tasksource/implicit-hate-stg1
+- metaeval/chaos-mnli-ambiguity
+- IlyaGusev/headline_cause
+- tasksource/logiqa-2.0-nli
+- tasksource/oasst2_dense_flat
+- sileod/mindgames
+- metaeval/ambient
+- metaeval/path-naturalness-prediction
+- civil_comments
+- AndyChiang/cloth
+- AndyChiang/dgen
+- tasksource/I2D2
+- webis/args_me
+- webis/Touche23-ValueEval
+- tasksource/starcon
+- PolyAI/banking77
+- tasksource/ConTRoL-nli
+- tasksource/tracie
+- tasksource/sherliic
+- tasksource/sen-making
+- tasksource/winowhy
+- tasksource/robustLR
+- CLUTRR/v1
+- tasksource/logical-fallacy
+- tasksource/parade
+- tasksource/cladder
+- tasksource/subjectivity
+- tasksource/MOH
+- tasksource/VUAC
+- tasksource/TroFi
+- sharc_modified
+- tasksource/conceptrules_v2
+- metaeval/disrpt
+- tasksource/zero-shot-label-nli
+- tasksource/com2sense
+- tasksource/scone
+- tasksource/winodict
+- tasksource/fool-me-twice
+- tasksource/monli
+- tasksource/corr2cause
+- lighteval/lsat_qa
+- tasksource/apt
+- zeroshot/twitter-financial-news-sentiment
+- tasksource/icl-symbol-tuning-instruct
+- tasksource/SpaceNLI
+- sihaochen/propsegment
+- HannahRoseKirk/HatemojiBuild
+- tasksource/regset
+- tasksource/esci
+- lmsys/chatbot_arena_conversations
+- neurae/dnd_style_intents
+- hitachi-nlp/FLD.v2
+- tasksource/SDOH-NLI
+- allenai/scifact_entailment
+- tasksource/feasibilityQA
+- tasksource/simple_pair
+- tasksource/AdjectiveScaleProbe-nli
+- tasksource/resnli
+- tasksource/SpaRTUN
+- tasksource/ReSQ
+- tasksource/semantic_fragments_nli
+- MoritzLaurer/dataset_train_nli
+- tasksource/stepgame
+- tasksource/nlgraph
+- tasksource/oasst2_pairwise_rlhf_reward
+- tasksource/hh-rlhf
+- tasksource/ruletaker
+- qbao775/PARARULE-Plus
+- tasksource/proofwriter
+- tasksource/logical-entailment
+- tasksource/nope
+- tasksource/LogicNLI
+- kiddothe2b/contract-nli
+- AshtonIsNotHere/nli4ct_semeval2024
+- tasksource/lsat-ar
+- tasksource/lsat-rc
+- AshtonIsNotHere/biosift-nli
+- tasksource/brainteasers
+- Anthropic/persuasion
+- erbacher/AmbigNQ-clarifying-question
+- tasksource/SIGA-nli
+- unigram/FOL-nli
+- tasksource/goal-step-wikihow
+- GGLab/PARADISE
+- tasksource/doc-nli
+- tasksource/mctest-nli
+- tasksource/patent-phrase-similarity
+- tasksource/natural-language-satisfiability
+- tasksource/idioms-nli
+- tasksource/lifecycle-entailment
+- nvidia/HelpSteer
+- nvidia/HelpSteer2
+- sadat2307/MSciNLI
+- pushpdeep/UltraFeedback-paired
+- tasksource/AES2-essay-scoring
+- tasksource/english-grading
+- tasksource/wice
+- Dzeniks/hover
+- tasksource/tasksource_dpo_pairs
+library_name: transformers
+pipeline_tag: zero-shot-classification
+tags:
+  - text-classification
+  - zero-shot-classification
+license: apache-2.0
+---
+
+# Model Card for Model ID
+
+deberta-v3-base with context length of 1280 fine-tuned on tasksource for 250k steps. I oversampled long NLI tasks (ConTRoL, doc-nli).
+Training data include helpsteer v1/v2, logical reasoning tasks (FOLIO, FOL-nli, LogicNLI...), OASST, hh/rlhf, linguistics oriented NLI tasks, tasksource-dpo, fact verification tasks.
+
+This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for:
+- Zero-shot entailment-based classification for arbitrary labels [ZS].
+- Natural language inference [NLI]
+- Further fine-tuning on a new task or tasksource task (classification, token classification, reward modeling or multiple-choice) [FT].
+
+| dataset                   |   accuracy |
+|:----------------------------|----------------:|
+| anli/a1                     |            63.3 |
+| anli/a2                     |            47.2 |
+| anli/a3                     |            49.4 |
+| nli_fever                   |            79.4 |
+| FOLIO                       |            61.8 |
+| ConTRoL-nli                 |            63.3 |
+| cladder                     |            71.1 |
+| zero-shot-label-nli         |            74.4 |
+| chatbot_arena_conversations |            72.2 |
+| oasst2_pairwise_rlhf_reward |            73.9 |
+| doc-nli                     |            90.0 |
+
+Zero-shot GPT-4 scores 61% on FOLIO (logical reasoning), 62% on cladder (probabilistic reasoning) and 56.4% on ConTRoL (long context NLI).
+
+# [ZS] Zero-shot classification pipeline
+```python
+from transformers import pipeline
+classifier = pipeline("zero-shot-classification",model="tasksource/deberta-base-long-nli")
+
+text = "one day I will see the world"
+candidate_labels = ['travel', 'cooking', 'dancing']
+classifier(text, candidate_labels)
+```
+NLI training data of this model includes [label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli), a NLI dataset specially constructed to improve this kind of zero-shot classification.
+
+# [NLI] Natural language inference pipeline
+
+```python
+from transformers import pipeline
+pipe = pipeline("text-classification",model="tasksource/deberta-base-long-nli")
+pipe([dict(text='there is a cat',
+  text_pair='there is a black cat')]) #list of (premise,hypothesis)
+# [{'label': 'neutral', 'score': 0.9952911138534546}]
+```
+
+# [TA] Tasksource-adapters: 1 line access to hundreds of tasks 
+
+```python
+# !pip install tasknet
+import tasknet as tn
+pipe = tn.load_pipeline('tasksource/deberta-base-long-nli','glue/sst2') # works for 500+ tasksource tasks
+pipe(['That movie was great !', 'Awful movie.'])
+# [{'label': 'positive', 'score': 0.9956}, {'label': 'negative', 'score': 0.9967}]
+```
+The list of tasks is available in model config.json.
+This is more efficient than ZS since it requires only one forward pass per example, but it is less flexible.
+
+
+# [FT] Tasknet: 3 lines fine-tuning
+
+```python
+# !pip install tasknet
+import tasknet as tn
+hparams=dict(model_name='tasksource/deberta-base-long-nli', learning_rate=2e-5)
+model, trainer = tn.Model_Trainer([tn.AutoTask("glue/rte")], hparams)
+trainer.train()
+```
+
+
+# Citation
+
+More details on this [article:](https://aclanthology.org/2024.lrec-main.1361/) 
+```
+@inproceedings{sileo-2024-tasksource,
+    title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
+    author = "Sileo, Damien",
+    editor = "Calzolari, Nicoletta  and
+      Kan, Min-Yen  and
+      Hoste, Veronique  and
+      Lenci, Alessandro  and
+      Sakti, Sakriani  and
+      Xue, Nianwen",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.1361",
+    pages = "15655--15684",
+}
+```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "tasksource/deberta-base-long-nli",
+  "_name_or_path": "microsoft/deberta-v3-base",
   "architectures": [
     "DebertaV2ForSequenceClassification"
   ],
@@ -1101,7 +1101,7 @@
     "blimp-2"
   ],
   "torch_dtype": "float32",
-  "transformers_version": "4.41.2",
+  "transformers_version": "4.42.3",
   "type_vocab_size": 0,
   "vocab_size": 128100
 }

model.safetensors

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+oid sha256:71cace06873870322ffa93e4005999961cef6d6b08263b01aad832ded23d94ae
+size 737722356

onnx/model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd8a820f52d46be08416c5449b75d75f4a9cd498f9130ad723dced4dedd3fc3e
+size 738566384

special_tokens_map.json

@@ -1,46 +1,10 @@
 {
-  "bos_token": {
-    "content": "[CLS]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "cls_token": {
-    "content": "[CLS]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "eos_token": {
-    "content": "[SEP]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "mask_token": {
-    "content": "[MASK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": {
-    "content": "[PAD]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "sep_token": {
-    "content": "[SEP]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
   "unk_token": {
     "content": "[UNK]",
     "lstrip": false,

tokenizer_config.json

@@ -47,19 +47,12 @@
   "do_lower_case": false,
   "eos_token": "[SEP]",
   "mask_token": "[MASK]",
-  "max_length": 1280,
   "model_max_length": 1000000000000000019884624838656,
-  "pad_to_multiple_of": null,
   "pad_token": "[PAD]",
-  "pad_token_type_id": 0,
-  "padding_side": "right",
   "sep_token": "[SEP]",
   "sp_model_kwargs": {},
   "split_by_punct": false,
-  "stride": 0,
   "tokenizer_class": "DebertaV2Tokenizer",
-  "truncation_side": "right",
-  "truncation_strategy": "longest_first",
   "unk_token": "[UNK]",
   "vocab_type": "spm"
 }