Text Classification
Transformers
Safetensors
modernbert
decision-model
candidate-scoring
calibration
text-embeddings-inference
Instructions to use skundu42/kev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skundu42/kev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="skundu42/kev")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("skundu42/kev") model = AutoModelForSequenceClassification.from_pretrained("skundu42/kev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download provenance.json from skundu42/kev: direct link, hf CLI and curl.
- Browser
- Download file 125 kB
-
https://huggingface.co/skundu42/kev/resolve/main/provenance.json
- Command line
-
hf download hf://skundu42/kev/provenance.json
-
curl -L -o provenance.json https://huggingface.co/skundu42/kev/resolve/main/provenance.json
125 kB
| { | |
| "status": "complete", | |
| "config": { | |
| "model_name_or_path": "jhu-clsp/ettin-encoder-400m", | |
| "model_revision": "7662476d60abb071a5bd319c9f3074f3072c062d", | |
| "max_length": 1024, | |
| "max_candidates": 16, | |
| "seed": 42, | |
| "per_device_train_batch_size": 1, | |
| "per_device_eval_batch_size": 1, | |
| "gradient_accumulation_steps": 32, | |
| "num_train_epochs": 1, | |
| "learning_rate": 2e-05, | |
| "weight_decay": 0.01, | |
| "warmup_ratio": 0.03, | |
| "bf16": true, | |
| "gradient_checkpointing": true, | |
| "eval_strategy": "steps", | |
| "save_strategy": "steps", | |
| "eval_steps": 500, | |
| "save_steps": 500, | |
| "logging_steps": 10, | |
| "save_total_limit": 2, | |
| "report_to": "none", | |
| "max_train_per_source": 50000, | |
| "max_train_per_task": 5000, | |
| "max_eval_per_source": 2000, | |
| "scan_limit_per_split": null | |
| }, | |
| "sources": [ | |
| { | |
| "dataset": "tasksource/zero-shot-label-nli", | |
| "revision": "ee693dba923b5d5484aa9232b7357c5e45dd39b8", | |
| "adapter": "zero_shot", | |
| "aggregate": true, | |
| "configs": [ | |
| "default" | |
| ], | |
| "splits": { | |
| "train": "train", | |
| "validation": "validation", | |
| "test": "test" | |
| }, | |
| "upstream_url": "https://huggingface.co/datasets/tasksource/zero-shot-label-nli", | |
| "license_terms_reference": "https://huggingface.co/datasets/tasksource/zero-shot-label-nli" | |
| }, | |
| { | |
| "dataset": "tasksource/tasksource-instruct-v0", | |
| "revision": "1dee7ed51b87880e37882c2cbed2d50bd114e0df", | |
| "adapter": "instruct", | |
| "aggregate": true, | |
| "configs": [ | |
| "default" | |
| ], | |
| "splits": { | |
| "train": "train", | |
| "validation": "validation", | |
| "test": "test" | |
| }, | |
| "upstream_url": "https://huggingface.co/datasets/tasksource/tasksource-instruct-v0", | |
| "license_terms_reference": "https://huggingface.co/datasets/tasksource/tasksource-instruct-v0" | |
| }, | |
| { | |
| "dataset": "nyu-mll/multi_nli", | |
| "revision": "da70db2af9d09693783c3320c4249840212ee221", | |
| "adapter": "nli", | |
| "configs": [ | |
| "default" | |
| ], | |
| "splits": { | |
| "train": "train", | |
| "validation_matched": "validation", | |
| "validation_mismatched": "validation" | |
| }, | |
| "upstream_url": "https://huggingface.co/datasets/nyu-mll/multi_nli", | |
| "license_terms_reference": "https://huggingface.co/datasets/nyu-mll/multi_nli" | |
| }, | |
| { | |
| "dataset": "facebook/anli", | |
| "revision": "8e4813d81f46d313dac7892e1c28076917cfcdf9", | |
| "adapter": "nli", | |
| "configs": [ | |
| "plain_text" | |
| ], | |
| "splits": { | |
| "train_r1": "train", | |
| "train_r2": "train", | |
| "train_r3": "train", | |
| "dev_r1": "validation", | |
| "dev_r2": "validation", | |
| "dev_r3": "validation", | |
| "test_r1": "test", | |
| "test_r2": "test", | |
| "test_r3": "test" | |
| }, | |
| "upstream_url": "https://huggingface.co/datasets/facebook/anli", | |
| "license_terms_reference": "https://huggingface.co/datasets/facebook/anli" | |
| }, | |
| { | |
| "dataset": "tasksource/defeasible-nli", | |
| "revision": "7c4a57df9d8de5c36d4e9caa977907b5e8469c4f", | |
| "adapter": "defeasible", | |
| "configs": [ | |
| "atomic", | |
| "snli", | |
| "social" | |
| ], | |
| "splits": { | |
| "train": "train", | |
| "validation": "validation", | |
| "test": "test" | |
| }, | |
| "upstream_url": "https://huggingface.co/datasets/tasksource/defeasible-nli", | |
| "license_terms_reference": "https://huggingface.co/datasets/tasksource/defeasible-nli" | |
| }, | |
| { | |
| "dataset": "tasksource/FOL-nli", | |
| "revision": "6b8a2ec01b226ed871fbde4677a31146ac5f370c", | |
| "adapter": "nli", | |
| "configs": [ | |
| "default" | |
| ], | |
| "splits": { | |
| "train": "train", | |
| "validation": "validation", | |
| "test": "test" | |
| }, | |
| "upstream_url": "https://huggingface.co/datasets/tasksource/FOL-nli", | |
| "license_terms_reference": "https://huggingface.co/datasets/tasksource/FOL-nli" | |
| }, | |
| { | |
| "dataset": "tasksource/doc-nli", | |
| "revision": "9b32389b6e21e232b8114f259f762f91fb486aff", | |
| "adapter": "doc_nli", | |
| "configs": [ | |
| "default" | |
| ], | |
| "splits": { | |
| "train": "train", | |
| "validation": "validation", | |
| "test": "test" | |
| }, | |
| "upstream_url": "https://huggingface.co/datasets/tasksource/doc-nli", | |
| "license_terms_reference": "https://huggingface.co/datasets/tasksource/doc-nli" | |
| }, | |
| { | |
| "dataset": "tasksource/bigbench", | |
| "revision": "210c156767d2f4f05d2f4fd0bb275017a67040fd", | |
| "adapter": "bigbench", | |
| "aggregate": true, | |
| "configs": [ | |
| "abstract_narrative_understanding", | |
| "anachronisms", | |
| "analogical_similarity", | |
| "analytic_entailment", | |
| "arithmetic", | |
| "ascii_word_recognition", | |
| "authorship_verification", | |
| "auto_categorization", | |
| "auto_debugging", | |
| "bbq_lite_json", | |
| "bridging_anaphora_resolution_barqa", | |
| "causal_judgment", | |
| "cause_and_effect", | |
| "checkmate_in_one", | |
| "chess_state_tracking", | |
| "chinese_remainder_theorem", | |
| "cifar10_classification", | |
| "code_line_description", | |
| "codenames", | |
| "color", | |
| "common_morpheme", | |
| "conceptual_combinations", | |
| "conlang_translation", | |
| "contextual_parametric_knowledge_conflicts", | |
| "crash_blossom", | |
| "crass_ai", | |
| "cryobiology_spanish", | |
| "cryptonite", | |
| "cs_algorithms", | |
| "dark_humor_detection", | |
| "date_understanding", | |
| "disambiguation_qa", | |
| "discourse_marker_prediction", | |
| "disfl_qa", | |
| "dyck_languages", | |
| "elementary_math_qa", | |
| "emoji_movie", | |
| "emojis_emotion_prediction", | |
| "empirical_judgments", | |
| "english_proverbs", | |
| "english_russian_proverbs", | |
| "entailed_polarity", | |
| "entailed_polarity_hindi", | |
| "epistemic_reasoning", | |
| "evaluating_information_essentiality", | |
| "fact_checker", | |
| "fantasy_reasoning", | |
| "few_shot_nlg", | |
| "figure_of_speech_detection", | |
| "formal_fallacies_syllogisms_negation", | |
| "gem", | |
| "gender_inclusive_sentences_german", | |
| "general_knowledge", | |
| "geometric_shapes", | |
| "goal_step_wikihow", | |
| "gre_reading_comprehension", | |
| "hhh_alignment", | |
| "hindi_question_answering", | |
| "hindu_knowledge", | |
| "hinglish_toxicity", | |
| "human_organs_senses", | |
| "hyperbaton", | |
| "identify_math_theorems", | |
| "identify_odd_metaphor", | |
| "implicatures", | |
| "implicit_relations", | |
| "indic_cause_and_effect", | |
| "intent_recognition", | |
| "international_phonetic_alphabet_nli", | |
| "international_phonetic_alphabet_transliterate", | |
| "intersect_geometry", | |
| "irony_identification", | |
| "kanji_ascii", | |
| "kannada", | |
| "key_value_maps", | |
| "known_unknowns", | |
| "language_games", | |
| "language_identification", | |
| "linguistic_mappings", | |
| "linguistics_puzzles", | |
| "list_functions", | |
| "logic_grid_puzzle", | |
| "logical_args", | |
| "logical_deduction", | |
| "logical_fallacy_detection", | |
| "logical_sequence", | |
| "mathematical_induction", | |
| "matrixshapes", | |
| "medical_questions_russian", | |
| "metaphor_boolean", | |
| "metaphor_understanding", | |
| "minute_mysteries_qa", | |
| "misconceptions", | |
| "misconceptions_russian", | |
| "mnist_ascii", | |
| "modified_arithmetic", | |
| "moral_permissibility", | |
| "movie_dialog_same_or_different", | |
| "movie_recommendation", | |
| "mult_data_wrangling", | |
| "navigate", | |
| "nonsense_words_grammar", | |
| "novel_concepts", | |
| "object_counting", | |
| "odd_one_out", | |
| "operators", | |
| "paragraph_segmentation", | |
| "parsinlu_qa", | |
| "parsinlu_reading_comprehension", | |
| "penguins_in_a_table", | |
| "periodic_elements", | |
| "persian_idioms", | |
| "phrase_relatedness", | |
| "physical_intuition", | |
| "physics", | |
| "physics_questions", | |
| "play_dialog_same_or_different", | |
| "polish_sequence_labeling", | |
| "presuppositions_as_nli", | |
| "qa_wikidata", | |
| "question_selection", | |
| "real_or_fake_text", | |
| "reasoning_about_colored_objects", | |
| "repeat_copy_logic", | |
| "rephrase", | |
| "rhyming", | |
| "riddle_sense", | |
| "ruin_names", | |
| "salient_translation_error_detection", | |
| "scientific_press_release", | |
| "semantic_parsing_in_context_sparc", | |
| "semantic_parsing_spider", | |
| "sentence_ambiguity", | |
| "similarities_abstraction", | |
| "simp_turing_concept", | |
| "simple_arithmetic_json", | |
| "simple_arithmetic_json_subtasks", | |
| "simple_ethical_questions", | |
| "simple_text_editing", | |
| "snarks", | |
| "social_iqa", | |
| "social_support", | |
| "sports_understanding", | |
| "strange_stories", | |
| "strategyqa", | |
| "sufficient_information", | |
| "suicide_risk", | |
| "swahili_english_proverbs", | |
| "swedish_to_german_proverbs", | |
| "symbol_interpretation", | |
| "tellmewhy", | |
| "temporal_sequences", | |
| "tense", | |
| "timedial", | |
| "topical_chat", | |
| "tracking_shuffled_objects", | |
| "understanding_fables", | |
| "undo_permutation", | |
| "unit_conversion", | |
| "unit_interpretation", | |
| "unnatural_in_context_learning", | |
| "vitaminc_fact_verification", | |
| "what_is_the_tao", | |
| "which_wiki_edit", | |
| "winowhy", | |
| "word_sorting", | |
| "word_unscrambling" | |
| ], | |
| "splits": { | |
| "train": "train", | |
| "validation": "validation" | |
| }, | |
| "upstream_url": "https://huggingface.co/datasets/tasksource/bigbench", | |
| "license_terms_reference": "https://huggingface.co/datasets/tasksource/bigbench" | |
| }, | |
| { | |
| "dataset": "Rowan/hellaswag", | |
| "revision": "218ec52e09a7e7462a5400043bb9a69a41d06b76", | |
| "adapter": "hellaswag", | |
| "configs": [ | |
| "default" | |
| ], | |
| "splits": { | |
| "train": "train", | |
| "validation": "validation" | |
| }, | |
| "excluded_splits": { | |
| "test": "Labels are hidden (empty strings)." | |
| }, | |
| "upstream_url": "https://huggingface.co/datasets/Rowan/hellaswag", | |
| "license_terms_reference": "https://huggingface.co/datasets/Rowan/hellaswag" | |
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| { | |
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| "revision": "35b264d03638db9f4ce671b711558bf7ff0f80d5", | |
| "adapter": "boolq", | |
| "configs": [ | |
| "default" | |
| ], | |
| "splits": { | |
| "train": "train", | |
| "validation": "validation" | |
| }, | |
| "upstream_url": "https://huggingface.co/datasets/google/boolq", | |
| "license_terms_reference": "https://huggingface.co/datasets/google/boolq" | |
| } | |
| ], | |
| "format_version": 1, | |
| "seed": 42, | |
| "scan_limit_per_split": null, | |
| "smoke_prefix_scan": false, | |
| "partition_policy": "Native test retained; native validation: 50/50 validation/calibration with test, otherwise 50/25/25 validation/calibration/test.", | |
| "dedup_policy": "Normalized state across tasks/sources; related content groups remain in one partition. Holdouts win, with test > calibration > validation > train.", | |
| "sampling_policy": "Lowest seeded row hashes per source/output partition, then aggregate task caps, then tokenization. Caps are maxima and are not backfilled after later filtering.", | |
| "limitations": "Smoke scans use only a prefix per native split; no claim of full-corpus leakage checking. Exact/group dedup does not detect paraphrases.", | |
| "max_eval_per_source_applies_to": "each output partition", | |
| "instruct_allowlist": [ | |
| "yelp_review_full/yelp_review_full", | |
| "tweet_eval/sentiment" | |
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