155 lines
3.1 KiB
INI
155 lines
3.1 KiB
INI
# fa_ent_news_lg — Persian NER with the lg-tier static floret vectors.
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#
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# Identical to configs/fa_ner_md.cfg (which is identical to fa_ner_sm.cfg except
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# include_static_vectors: true). Only the vector table supplied at train time via
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# --paths.vectors differs: fa_floret, 200k rows x 300d, floret mode, trained on the full
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# Persian Wikipedia dump for 5 epochs (assets/vectors/fa_floret_lg), vs md's 50k rows x 300d
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# trained on 400k Persian documents.
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#
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# Same embedded-tok2vec design as sm/md (no Tok2VecListener), so the trained component stays
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# sourceable into a future fa_core_news_lg via `nlp.add_pipe("ner", source=...)`.
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[paths]
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train = null
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dev = null
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vectors = null
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init_tok2vec = null
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[system]
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gpu_allocator = null
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seed = 0
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[nlp]
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lang = "fa"
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pipeline = ["ner"]
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batch_size = 1000
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disabled = []
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before_creation = null
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after_creation = null
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after_pipeline_creation = null
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[nlp.tokenizer]
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@tokenizers = "spacy.Tokenizer.v1"
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[nlp.vectors]
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@vectors = "spacy.Vectors.v1"
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[components]
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[components.ner]
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factory = "ner"
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moves = null
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update_with_oracle_cut_size = 100
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incorrect_spans_key = null
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[components.ner.model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "ner"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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use_upper = true
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nO = null
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[components.ner.model.tok2vec]
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@architectures = "spacy.Tok2Vec.v2"
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[components.ner.model.tok2vec.embed]
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@architectures = "spacy.MultiHashEmbed.v2"
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width = ${components.ner.model.tok2vec.encode.width}
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attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
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rows = [5000, 1000, 2500, 2500]
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include_static_vectors = true
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[components.ner.model.tok2vec.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = 96
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depth = 4
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window_size = 1
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maxout_pieces = 3
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[components.ner.scorer]
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@scorers = "spacy.ner_scorer.v1"
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[corpora]
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[corpora.train]
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@readers = "spacy.Corpus.v1"
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path = ${paths.train}
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max_length = 0
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gold_preproc = false
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limit = 0
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augmenter = null
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[corpora.dev]
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@readers = "spacy.Corpus.v1"
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path = ${paths.dev}
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max_length = 0
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gold_preproc = false
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limit = 0
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augmenter = null
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[training]
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dev_corpus = "corpora.dev"
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train_corpus = "corpora.train"
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seed = ${system.seed}
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gpu_allocator = ${system.gpu_allocator}
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dropout = 0.1
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accumulate_gradient = 1
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patience = 1600
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max_epochs = 0
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max_steps = 20000
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eval_frequency = 400
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frozen_components = []
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annotating_components = []
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before_to_disk = null
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before_update = null
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[training.optimizer]
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@optimizers = "Adam.v1"
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beta1 = 0.9
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beta2 = 0.999
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L2_is_weight_decay = true
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L2 = 0.01
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grad_clip = 1.0
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use_averages = false
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eps = 1e-08
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learn_rate = 0.001
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[training.batcher]
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@batchers = "spacy.batch_by_words.v1"
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discard_oversize = false
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tolerance = 0.2
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get_length = null
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[training.batcher.size]
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@schedules = "compounding.v1"
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start = 100
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stop = 1000
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compound = 1.001
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t = 0.0
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[training.logger]
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@loggers = "spacy.ConsoleLogger.v1"
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progress_bar = false
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[training.score_weights]
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ents_f = 1.0
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ents_p = 0.0
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ents_r = 0.0
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ents_per_type = null
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[initialize]
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vectors = ${paths.vectors}
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init_tok2vec = ${paths.init_tok2vec}
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vocab_data = null
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lookups = null
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before_init = null
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after_init = null
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[initialize.tokenizer]
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[initialize.components]
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[pretraining]
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