spaCy/examples/training/train_textcat_config.cfg

[nlp]
lang = "en"

[nlp.pipeline.textcat]
factory = "textcat"

[nlp.pipeline.textcat.model]
@architectures = "spacy.TextCatCNN.v1"
exclusive_classes = false

[nlp.pipeline.textcat.model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 96
depth = 4
embed_size = 2000
window_size = 1
maxout_pieces = 3
subword_features = true
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			`[nlp]`
			`lang = "en"`

			`[nlp.pipeline.textcat]`
			`factory = "textcat"`

			`[nlp.pipeline.textcat.model]`
			`@architectures = "spacy.TextCatCNN.v1"`
			`exclusive_classes = false`

			`[nlp.pipeline.textcat.model.tok2vec]`
			`@architectures = "spacy.HashEmbedCNN.v1"`
			`pretrained_vectors = null`
			`width = 96`
			`depth = 4`
			`embed_size = 2000`
			`window_size = 1`
			`maxout_pieces = 3`
			`subword_features = true`