spaCy/spacy/pipeline/defaults/textcat_defaults.cfg

[model]
@architectures = "spacy.TextCat.v1"
exclusive_classes = false
pretrained_vectors = null
width = 64
conv_depth = 2
embed_size = 2000
window_size = 1
ngram_size = 1
dropout = null
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add .cfg to manifest fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build 2020-02-27 20:42:27 +03:00			`[model]`
Train textcat with config (#5143) * bring back default build_text_classifier method * remove _set_dims_ hack in favor of proper dim inference * add tok2vec initialize to unit test * small fixes * add unit test for various textcat config settings * logistic output layer does not have nO * fix window_size setting * proper fix * fix W initialization * Update textcat training example * Use ml_datasets * Convert training data to `Example` format * Use `n_texts` to set proportionate dev size * fix _init renaming on latest thinc * avoid setting a non-existing dim * update to thinc==8.0.0a2 * add BOW and CNN defaults for easy testing * various experiments with train_textcat script, fix softmax activation in textcat bow * allow textcat train script to work on other datasets as well * have dataset as a parameter * train textcat from config, with example config * add config for training textcat * formatting * fix exclusive_classes * fixing BOW for GPU * bump thinc to 8.0.0a3 (not published yet so CI will fail) * add in link_vectors_to_models which got deleted Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> 2020-03-29 20:40:36 +03:00			`@architectures = "spacy.TextCat.v1"`
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add .cfg to manifest fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build 2020-02-27 20:42:27 +03:00			`exclusive_classes = false`
			`pretrained_vectors = null`
Train textcat with config (#5143) * bring back default build_text_classifier method * remove _set_dims_ hack in favor of proper dim inference * add tok2vec initialize to unit test * small fixes * add unit test for various textcat config settings * logistic output layer does not have nO * fix window_size setting * proper fix * fix W initialization * Update textcat training example * Use ml_datasets * Convert training data to `Example` format * Use `n_texts` to set proportionate dev size * fix _init renaming on latest thinc * avoid setting a non-existing dim * update to thinc==8.0.0a2 * add BOW and CNN defaults for easy testing * various experiments with train_textcat script, fix softmax activation in textcat bow * allow textcat train script to work on other datasets as well * have dataset as a parameter * train textcat from config, with example config * add config for training textcat * formatting * fix exclusive_classes * fixing BOW for GPU * bump thinc to 8.0.0a3 (not published yet so CI will fail) * add in link_vectors_to_models which got deleted Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> 2020-03-29 20:40:36 +03:00			`width = 64`
			`conv_depth = 2`
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add .cfg to manifest fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build 2020-02-27 20:42:27 +03:00			`embed_size = 2000`
			`window_size = 1`
Train textcat with config (#5143) * bring back default build_text_classifier method * remove _set_dims_ hack in favor of proper dim inference * add tok2vec initialize to unit test * small fixes * add unit test for various textcat config settings * logistic output layer does not have nO * fix window_size setting * proper fix * fix W initialization * Update textcat training example * Use ml_datasets * Convert training data to `Example` format * Use `n_texts` to set proportionate dev size * fix _init renaming on latest thinc * avoid setting a non-existing dim * update to thinc==8.0.0a2 * add BOW and CNN defaults for easy testing * various experiments with train_textcat script, fix softmax activation in textcat bow * allow textcat train script to work on other datasets as well * have dataset as a parameter * train textcat from config, with example config * add config for training textcat * formatting * fix exclusive_classes * fixing BOW for GPU * bump thinc to 8.0.0a3 (not published yet so CI will fail) * add in link_vectors_to_models which got deleted Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> 2020-03-29 20:40:36 +03:00			`ngram_size = 1`
formatting 2020-06-04 17:09:55 +03:00			`dropout = null`