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spaCy/extra/experiments/tok2vec-ner/multihashembed_tok2vec.cfg

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Tok2Vec: extract-embed-encode (#5102) * avoid changing original config * fix elif structure, batch with just int crashes otherwise * tok2vec example with doc2feats, encode and embed architectures * further clean up MultiHashEmbed * further generalize Tok2Vec to work with extract-embed-encode parts * avoid initializing the charembed layer with Docs (for now ?) * small fixes for bilstm config (still does not run) * rename to core layer * move new configs * walk model to set nI instead of using core ref * fix senter overfitting test to be more similar to the training data (avoid flakey behaviour)
2020-03-08 12:23:18 +00:00
[training]
use_gpu = -1
limit = 0
dropout = 0.2
patience = 10000
eval_frequency = 200
Adapt parser and NER for transformers (#5449) * Draft layer for BILUO actions * Fixes to biluo layer * WIP on BILUO layer * Add tests for BILUO layer * Format * Fix transitions * Update test * Link in the simple_ner * Update BILUO tagger * Update __init__ * Import simple_ner * Update test * Import * Add files * Add config * Fix label passing for BILUO and tagger * Fix label handling for simple_ner component * Update simple NER test * Update config * Hack train script * Update BILUO layer * Fix SimpleNER component * Update train_from_config * Add biluo_to_iob helper * Add IOB layer * Add IOBTagger model * Update biluo layer * Update SimpleNER tagger * Update BILUO * Read random seed in train-from-config * Update use of normal_init * Fix normalization of gradient in SimpleNER * Update IOBTagger * Remove print * Tweak masking in BILUO * Add dropout in SimpleNER * Update thinc * Tidy up simple_ner * Fix biluo model * Unhack train-from-config * Update setup.cfg and requirements * Add tb_framework.py for parser model * Try to avoid memory leak in BILUO * Move ParserModel into spacy.ml, avoid need for subclass. * Use updated parser model * Remove incorrect call to model.initializre in PrecomputableAffine * Update parser model * Avoid divide by zero in tagger * Add extra dropout layer in tagger * Refine minibatch_by_words function to avoid oom * Fix parser model after refactor * Try to avoid div-by-zero in SimpleNER * Fix infinite loop in minibatch_by_words * Use SequenceCategoricalCrossentropy in Tagger * Fix parser model when hidden layer * Remove extra dropout from tagger * Add extra nan check in tagger * Fix thinc version * Update tests and imports * Fix test * Update test * Update tests * Fix tests * Fix test Co-authored-by: Ines Montani <ines@ines.io>
2020-05-18 20:23:33 +00:00
scores = ["ents_p", "ents_r", "ents_f"]
Tok2Vec: extract-embed-encode (#5102) * avoid changing original config * fix elif structure, batch with just int crashes otherwise * tok2vec example with doc2feats, encode and embed architectures * further clean up MultiHashEmbed * further generalize Tok2Vec to work with extract-embed-encode parts * avoid initializing the charembed layer with Docs (for now ?) * small fixes for bilstm config (still does not run) * rename to core layer * move new configs * walk model to set nI instead of using core ref * fix senter overfitting test to be more similar to the training data (avoid flakey behaviour)
2020-03-08 12:23:18 +00:00
score_weights = {"ents_f": 1}
orth_variant_level = 0.0
gold_preproc = true
max_length = 0
Various fixes to NEL functionality, Example class etc (#5460) * setting KB in the EL constructor, similar to how the model is passed on * removing wikipedia example files - moved to projects * throw an error when nlp.update is called with 2 positional arguments * rewriting the config logic in create pipe to accomodate for other objects (e.g. KB) in the config * update config files with new parameters * avoid training pipeline components that don't have a model (like sentencizer) * various small fixes + UX improvements * small fixes * set thinc to 8.0.0a9 everywhere * remove outdated comment
2020-05-20 09:41:12 +00:00
seed = 0
accumulate_gradient = 2
add discard_oversize parameter, move optimizer to training subsection
2020-06-03 08:04:16 +00:00
discard_oversize = false
Adapt parser and NER for transformers (#5449) * Draft layer for BILUO actions * Fixes to biluo layer * WIP on BILUO layer * Add tests for BILUO layer * Format * Fix transitions * Update test * Link in the simple_ner * Update BILUO tagger * Update __init__ * Import simple_ner * Update test * Import * Add files * Add config * Fix label passing for BILUO and tagger * Fix label handling for simple_ner component * Update simple NER test * Update config * Hack train script * Update BILUO layer * Fix SimpleNER component * Update train_from_config * Add biluo_to_iob helper * Add IOB layer * Add IOBTagger model * Update biluo layer * Update SimpleNER tagger * Update BILUO * Read random seed in train-from-config * Update use of normal_init * Fix normalization of gradient in SimpleNER * Update IOBTagger * Remove print * Tweak masking in BILUO * Add dropout in SimpleNER * Update thinc * Tidy up simple_ner * Fix biluo model * Unhack train-from-config * Update setup.cfg and requirements * Add tb_framework.py for parser model * Try to avoid memory leak in BILUO * Move ParserModel into spacy.ml, avoid need for subclass. * Use updated parser model * Remove incorrect call to model.initializre in PrecomputableAffine * Update parser model * Avoid divide by zero in tagger * Add extra dropout layer in tagger * Refine minibatch_by_words function to avoid oom * Fix parser model after refactor * Try to avoid div-by-zero in SimpleNER * Fix infinite loop in minibatch_by_words * Use SequenceCategoricalCrossentropy in Tagger * Fix parser model when hidden layer * Remove extra dropout from tagger * Add extra nan check in tagger * Fix thinc version * Update tests and imports * Fix test * Update test * Update tests * Fix tests * Fix test Co-authored-by: Ines Montani <ines@ines.io>
2020-05-18 20:23:33 +00:00
[training.batch_size]
@schedules = "compounding.v1"
start = 3000
stop = 3000
compound = 1.001
Tok2Vec: extract-embed-encode (#5102) * avoid changing original config * fix elif structure, batch with just int crashes otherwise * tok2vec example with doc2feats, encode and embed architectures * further clean up MultiHashEmbed * further generalize Tok2Vec to work with extract-embed-encode parts * avoid initializing the charembed layer with Docs (for now ?) * small fixes for bilstm config (still does not run) * rename to core layer * move new configs * walk model to set nI instead of using core ref * fix senter overfitting test to be more similar to the training data (avoid flakey behaviour)
2020-03-08 12:23:18 +00:00
add discard_oversize parameter, move optimizer to training subsection
2020-06-03 08:04:16 +00:00
[training.optimizer]
Tok2Vec: extract-embed-encode (#5102) * avoid changing original config * fix elif structure, batch with just int crashes otherwise * tok2vec example with doc2feats, encode and embed architectures * further clean up MultiHashEmbed * further generalize Tok2Vec to work with extract-embed-encode parts * avoid initializing the charembed layer with Docs (for now ?) * small fixes for bilstm config (still does not run) * rename to core layer * move new configs * walk model to set nI instead of using core ref * fix senter overfitting test to be more similar to the training data (avoid flakey behaviour)
2020-03-08 12:23:18 +00:00
@optimizers = "Adam.v1"
learn_rate = 0.001
beta1 = 0.9
beta2 = 0.999
[nlp]
lang = "en"
vectors = null
[nlp.pipeline.ner]
Remove simple_ner code (#6041) * remove simple_ner code * remove unused _biluo and _iob files
2020-09-09 14:11:27 +00:00
factory = "ner"
Tok2Vec: extract-embed-encode (#5102) * avoid changing original config * fix elif structure, batch with just int crashes otherwise * tok2vec example with doc2feats, encode and embed architectures * further clean up MultiHashEmbed * further generalize Tok2Vec to work with extract-embed-encode parts * avoid initializing the charembed layer with Docs (for now ?) * small fixes for bilstm config (still does not run) * rename to core layer * move new configs * walk model to set nI instead of using core ref * fix senter overfitting test to be more similar to the training data (avoid flakey behaviour)
2020-03-08 12:23:18 +00:00
[nlp.pipeline.ner.model]
Remove simple_ner code (#6041) * remove simple_ner code * remove unused _biluo and _iob files
2020-09-09 14:11:27 +00:00
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 6
hidden_width = 64
maxout_pieces = 2
Tok2Vec: extract-embed-encode (#5102) * avoid changing original config * fix elif structure, batch with just int crashes otherwise * tok2vec example with doc2feats, encode and embed architectures * further clean up MultiHashEmbed * further generalize Tok2Vec to work with extract-embed-encode parts * avoid initializing the charembed layer with Docs (for now ?) * small fixes for bilstm config (still does not run) * rename to core layer * move new configs * walk model to set nI instead of using core ref * fix senter overfitting test to be more similar to the training data (avoid flakey behaviour)
2020-03-08 12:23:18 +00:00
[nlp.pipeline.ner.model.tok2vec]
Adapt parser and NER for transformers (#5449) * Draft layer for BILUO actions * Fixes to biluo layer * WIP on BILUO layer * Add tests for BILUO layer * Format * Fix transitions * Update test * Link in the simple_ner * Update BILUO tagger * Update __init__ * Import simple_ner * Update test * Import * Add files * Add config * Fix label passing for BILUO and tagger * Fix label handling for simple_ner component * Update simple NER test * Update config * Hack train script * Update BILUO layer * Fix SimpleNER component * Update train_from_config * Add biluo_to_iob helper * Add IOB layer * Add IOBTagger model * Update biluo layer * Update SimpleNER tagger * Update BILUO * Read random seed in train-from-config * Update use of normal_init * Fix normalization of gradient in SimpleNER * Update IOBTagger * Remove print * Tweak masking in BILUO * Add dropout in SimpleNER * Update thinc * Tidy up simple_ner * Fix biluo model * Unhack train-from-config * Update setup.cfg and requirements * Add tb_framework.py for parser model * Try to avoid memory leak in BILUO * Move ParserModel into spacy.ml, avoid need for subclass. * Use updated parser model * Remove incorrect call to model.initializre in PrecomputableAffine * Update parser model * Avoid divide by zero in tagger * Add extra dropout layer in tagger * Refine minibatch_by_words function to avoid oom * Fix parser model after refactor * Try to avoid div-by-zero in SimpleNER * Fix infinite loop in minibatch_by_words * Use SequenceCategoricalCrossentropy in Tagger * Fix parser model when hidden layer * Remove extra dropout from tagger * Add extra nan check in tagger * Fix thinc version * Update tests and imports * Fix test * Update test * Update tests * Fix tests * Fix test Co-authored-by: Ines Montani <ines@ines.io>
2020-05-18 20:23:33 +00:00
@architectures = "spacy.HashEmbedCNN.v1"
width = 128
depth = 4
embed_size = 7000
maxout_pieces = 3
window_size = 1
subword_features = true
pretrained_vectors = null
update config files with separate dropout for Tok2Vec layer
2020-06-03 09:53:59 +00:00
dropout = null