Initial dummy model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -1,3 +1,49 @@
 ---
-license: cc-by-nc-4.0
+license: apache-2.0
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+pipeline_tag: text-classification
 ---
+
+# /scratch/work/koutchc1/experiments/staqt
+
+This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Usage
+
+To use this model for inference, first install the SetFit library:
+
+```bash
+python -m pip install setfit
+```
+
+You can then run inference as follows:
+
+```python
+from setfit import SetFitModel
+
+# Download from Hub and run inference
+model = SetFitModel.from_pretrained("/scratch/work/koutchc1/experiments/staqt")
+# Run inference
+preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
+```
+
+## BibTeX entry and citation info
+
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+doi = {10.48550/ARXIV.2209.11055},
+url = {https://arxiv.org/abs/2209.11055},
+author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+title = {Efficient Few-Shot Learning Without Prompts},
+publisher = {arXiv},
+year = {2022},
+copyright = {Creative Commons Attribution 4.0 International}
+}
+```

added_tokens.json

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+{
+  "[MASK]": 128000
+}

config.json

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+    "glue/qqp",
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+    "sick/label",
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+    "sick/entailment_AB",
+    "snli",
+    "scitail/snli_format",
+    "hans",
+    "WANLI",
+    "recast/recast_factuality",
+    "recast/recast_verbcorner",
+    "recast/recast_puns",
+    "recast/recast_kg_relations",
+    "recast/recast_ner",
+    "recast/recast_sentiment",
+    "recast/recast_megaveridicality",
+    "recast/recast_verbnet",
+    "probability_words_nli/usnli",
+    "probability_words_nli/reasoning_2hop",
+    "probability_words_nli/reasoning_1hop",
+    "nan-nli/joey234--nan-nli",
+    "nli_fever",
+    "breaking_nli",
+    "conj_nli",
+    "fracas",
+    "dialogue_nli",
+    "mpe",
+    "dnc",
+    "gpt3_nli",
+    "recast_white/fnplus",
+    "recast_white/sprl",
+    "recast_white/dpr",
+    "joci",
+    "contrast_nli",
+    "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_quantifiers/log",
+    "imppres/implicature_numerals_2_3/log",
+    "imppres/implicature_numerals_10_100/log",
+    "imppres/implicature_modals/log",
+    "imppres/implicature_gradable_verb/log",
+    "imppres/implicature_gradable_adjective/log",
+    "imppres/implicature_connectives/log",
+    "glue_diagnostics/diagnostics",
+    "hlgd",
+    "paws/labeled_final",
+    "paws/labeled_swap",
+    "quora",
+    "medical_questions_pairs",
+    "conll2003/pos_tags",
+    "conll2003/chunk_tags",
+    "conll2003/ner_tags",
+    "hh-rlhf",
+    "model-written-evals",
+    "truthful_qa/multiple_choice",
+    "fig-qa",
+    "bigbench/similarities_abstraction",
+    "bigbench/tracking_shuffled_objects",
+    "bigbench/dyck_languages",
+    "bigbench/cause_and_effect",
+    "bigbench/contextual_parametric_knowledge_conflicts",
+    "bigbench/logical_deduction",
+    "bigbench/movie_recommendation",
+    "bigbench/hhh_alignment",
+    "bigbench/checkmate_in_one",
+    "bigbench/discourse_marker_prediction",
+    "bigbench/identify_odd_metaphor",
+    "bigbench/entailed_polarity",
+    "bigbench/novel_concepts",
+    "bigbench/nonsense_words_grammar",
+    "bigbench/goal_step_wikihow",
+    "bigbench/sports_understanding",
+    "bigbench/moral_permissibility",
+    "bigbench/code_line_description",
+    "bigbench/odd_one_out",
+    "bigbench/empirical_judgments",
+    "bigbench/gre_reading_comprehension",
+    "bigbench/reasoning_about_colored_objects",
+    "bigbench/identify_math_theorems",
+    "bigbench/abstract_narrative_understanding",
+    "bigbench/date_understanding",
+    "bigbench/play_dialog_same_or_different",
+    "bigbench/real_or_fake_text",
+    "bigbench/crass_ai",
+    "bigbench/international_phonetic_alphabet_nli",
+    "bigbench/presuppositions_as_nli",
+    "bigbench/ruin_names",
+    "bigbench/sentence_ambiguity",
+    "bigbench/intent_recognition",
+    "bigbench/analytic_entailment",
+    "bigbench/conceptual_combinations",
+    "bigbench/implicit_relations",
+    "bigbench/emojis_emotion_prediction",
+    "bigbench/hindu_knowledge",
+    "bigbench/elementary_math_qa",
+    "bigbench/mathematical_induction",
+    "bigbench/irony_identification",
+    "bigbench/authorship_verification",
+    "bigbench/disambiguation_qa",
+    "bigbench/general_knowledge",
+    "bigbench/known_unknowns",
+    "bigbench/metaphor_boolean",
+    "bigbench/logical_args",
+    "bigbench/metaphor_understanding",
+    "bigbench/arithmetic",
+    "bigbench/salient_translation_error_detection",
+    "bigbench/human_organs_senses",
+    "bigbench/formal_fallacies_syllogisms_negation",
+    "bigbench/fantasy_reasoning",
+    "bigbench/fact_checker",
+    "bigbench/unit_interpretation",
+    "bigbench/vitaminc_fact_verification",
+    "bigbench/cifar10_classification",
+    "bigbench/social_iqa",
+    "bigbench/emoji_movie",
+    "bigbench/dark_humor_detection",
+    "bigbench/understanding_fables",
+    "bigbench/symbol_interpretation",
+    "bigbench/social_support",
+    "bigbench/logical_fallacy_detection",
+    "bigbench/causal_judgment",
+    "bigbench/strategyqa",
+    "bigbench/epistemic_reasoning",
+    "bigbench/anachronisms",
+    "bigbench/mnist_ascii",
+    "bigbench/crash_blossom",
+    "bigbench/english_proverbs",
+    "bigbench/cs_algorithms",
+    "bigbench/simple_ethical_questions",
+    "bigbench/hyperbaton",
+    "bigbench/key_value_maps",
+    "bigbench/physics",
+    "bigbench/figure_of_speech_detection",
+    "bigbench/undo_permutation",
+    "bigbench/timedial",
+    "bigbench/geometric_shapes",
+    "bigbench/analogical_similarity",
+    "bigbench/color",
+    "bigbench/question_selection",
+    "bigbench/evaluating_information_essentiality",
+    "bigbench/phrase_relatedness",
+    "bigbench/logic_grid_puzzle",
+    "bigbench/misconceptions",
+    "bigbench/winowhy",
+    "bigbench/snarks",
+    "bigbench/temporal_sequences",
+    "bigbench/riddle_sense",
+    "bigbench/implicatures",
+    "bigbench/logical_sequence",
+    "bigbench/suicide_risk",
+    "bigbench/physical_intuition",
+    "bigbench/movie_dialog_same_or_different",
+    "bigbench/navigate",
+    "bigbench/penguins_in_a_table",
+    "bigbench/strange_stories",
+    "bigbench/bbq_lite_json",
+    "cos_e/v1.0",
+    "cosmos_qa",
+    "dream",
+    "openbookqa",
+    "qasc",
+    "quartz",
+    "quail",
+    "head_qa/en",
+    "sciq",
+    "social_i_qa",
+    "wiki_hop",
+    "wiqa",
+    "piqa",
+    "hellaswag",
+    "super_glue/copa",
+    "balanced-copa",
+    "e-CARE",
+    "art",
+    "winogrande/winogrande_xl",
+    "codah/codah",
+    "ai2_arc/ARC-Easy/challenge",
+    "ai2_arc/ARC-Challenge/challenge",
+    "definite_pronoun_resolution",
+    "swag",
+    "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/emoji",
+    "tweet_eval/emotion",
+    "tweet_eval/hate",
+    "tweet_eval/offensive",
+    "tweet_eval/sentiment",
+    "tweet_eval/irony",
+    "tweet_eval/stance_atheism",
+    "tweet_eval/stance_climate",
+    "tweet_eval/stance_hillary",
+    "tweet_eval/stance_feminist",
+    "tweet_eval/stance_abortion",
+    "discovery/discovery",
+    "pragmeval/squinky-informativeness",
+    "pragmeval/verifiability",
+    "pragmeval/squinky-formality",
+    "pragmeval/squinky-implicature",
+    "pragmeval/emobank-dominance",
+    "pragmeval/emobank-arousal",
+    "pragmeval/switchboard",
+    "pragmeval/mrda",
+    "pragmeval/emobank-valence",
+    "pragmeval/persuasiveness-premisetype",
+    "pragmeval/persuasiveness-relevance",
+    "pragmeval/pdtb",
+    "pragmeval/stac",
+    "pragmeval/persuasiveness-strength",
+    "pragmeval/sarcasm",
+    "pragmeval/gum",
+    "pragmeval/persuasiveness-eloquence",
+    "pragmeval/persuasiveness-claimtype",
+    "pragmeval/persuasiveness-specificity",
+    "pragmeval/emergent",
+    "silicone/maptask",
+    "silicone/oasis",
+    "silicone/meld_s",
+    "silicone/meld_e",
+    "silicone/iemocap",
+    "silicone/dyda_e",
+    "silicone/dyda_da",
+    "silicone/sem",
+    "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",
+    "banking77",
+    "hate_speech_offensive",
+    "yahoo_answers_topics",
+    "stackoverflow-questions",
+    "hyperpartisan_news",
+    "sciie",
+    "citation_intent",
+    "go_emotions/simplified",
+    "scicite",
+    "liar",
+    "lexical_relation_classification/CogALexV",
+    "lexical_relation_classification/EVALution",
+    "lexical_relation_classification/K&H+N",
+    "lexical_relation_classification/ROOT09",
+    "lexical_relation_classification/BLESS",
+    "linguisticprobing/top_constituents",
+    "linguisticprobing/bigram_shift",
+    "linguisticprobing/odd_man_out",
+    "linguisticprobing/coordination_inversion",
+    "linguisticprobing/sentence_length",
+    "linguisticprobing/past_present",
+    "linguisticprobing/obj_number",
+    "linguisticprobing/subj_number",
+    "linguisticprobing/tree_depth",
+    "crowdflower/economic-news",
+    "crowdflower/text_emotion",
+    "crowdflower/political-media-message",
+    "crowdflower/sentiment_nuclear_power",
+    "crowdflower/political-media-bias",
+    "crowdflower/political-media-audience",
+    "crowdflower/tweet_global_warming",
+    "crowdflower/airline-sentiment",
+    "crowdflower/corporate-messaging",
+    "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/horoscope",
+    "blog_authorship_corpus/job",
+    "open_question_type",
+    "health_fact",
+    "commonsense_qa",
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