File size: 4,223 Bytes
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"model": "distilbert",
"labels": [
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
"B-MISC",
"I-MISC"
],
"length_policy": {
"max_subwords": 256,
"word_policy": "No recurrent word truncation; packed dynamic batches",
"char_max": 61,
"char_policy": "Train max; held-out/app longer chars clipped, count reported",
"transformer_policy": "Greedy whole-word chunks; no overlap; label-independent; reconstruct before scoring",
"app_recurrent_chunk_words": 256,
"app_max_characters": 20000,
"app_sentence_policy": "raw-v2: acronyms/titles and hyphenated words retained; format/control characters omitted from inference with original offsets preserved; standalone .!? and newlines split sentences"
},
"selection": {
"winner": "distilbert",
"best_validation_f1": 0.9391641388608022,
"eligible_within_0.001": [
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],
"rule": "Strict validation micro F1; within absolute 0.001 prefer fewer trainable parameters, then measured validation seconds, then stable model name",
"experiment_id": "8a1b26004cfaa53318dee7fbb6289ba971e3152c3aaaf7171b08086838f4aac2",
"mode": "full",
"checkpoint_manifests": {
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"lstm": "55fd4158f08af157b3466c47fc5c2ed50f295a29f7e6e390107d699146733e5a",
"bilstm": "ed628fb397d73a3d130e5c91460d0b340402dcbea621cc3a7898e9d62606d04a",
"bilstm_crf": "81979fc4eff8545034b555f1c4906f0e785bcdc70a257df0668a3a2cb85ebb5a"
}
},
"experiment_id": "8a1b26004cfaa53318dee7fbb6289ba971e3152c3aaaf7171b08086838f4aac2",
"preprocessing": "Preserve casing/punctuation/word order; train-only vocab; lowercase only external GloVe lookup. No POS/chunk features.",
"span_policy": "Strict IOB2; invalid I predictions ignored rather than repaired. Half-open original character offsets.",
"app_tokenizer": "raw-v2: acronyms/titles and hyphenated words retained; format/control characters omitted from inference with original offsets preserved; standalone .!? and newlines split sentences",
"benchmark_tokenization_note": "Raw-text regex is deterministic but differs from human CoNLL tokenization; exported token API preserves benchmark tokens exactly.",
"embedding_provenance": {
"num_records": 400000,
"file_size": 69182535,
"base_dataset": "Wikipedia 2014 + Gigaword 5 (6B tokens, uncased)",
"reader_code": "https://github.com/RaRe-Technologies/gensim-data/releases/download/glove-wiki-gigaword-50/__init__.py",
"license": "http://opendatacommons.org/licenses/pddl/",
"parameters": {
"dimension": 50
},
"description": "Pre-trained vectors based on Wikipedia 2014 + Gigaword, 5.6B tokens, 400K vocab, uncased (https://nlp.stanford.edu/projects/glove/).",
"preprocessing": "Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-50.txt`.",
"read_more": [
"https://nlp.stanford.edu/projects/glove/",
"https://nlp.stanford.edu/pubs/glove.pdf"
],
"checksum": "c289bc5d7f2f02c6dc9f2f9b67641813",
"file_name": "glove-wiki-gigaword-50.gz",
"parts": 1
},
"training_mode": "full",
"full_training_results": true,
"inference_update": {
"revision": "raw-v2",
"date": "2026-11-10",
"training_experiment_unchanged": true,
"previous_app_archive": {
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"sha256": "079a09e2e8239c28f8818b414ba7810e8e51433016f7098d00f0dcae12c986b1"
},
"source_sha256": {
"common.py": "33b5d2a9dbb97e86ad46cf79c41433f8525ac76494ff9966f908f456d408e3a3",
"inference.py": "0286bfa88af230330333402052bc6ddc58581f212fc039e78cc32889ba1e418f",
"app.py": "09e7845cdfd24e85eca8cb34a5f043e8fee7a047866fe892352944fabe4acc30",
"models.py": "05519b51d548b7cdca9df3248d51d44e0916937f0711fb4ce3913bc0a6a25cb0"
},
"verification": "All 6 raw-text and offline CPU checkpoint regression tests passed, including former crashes, full acronym/hyphen boundaries, offsets and long input. Updated Kaggle training has not been rerun."
}
} |