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Update train command and add docs on hyperparameters
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@ -166,7 +166,7 @@ p
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| #[+a("/docs/api/annotation#json-input") JSON format].
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+code(false, "bash").
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python -m spacy train [lang] [output_dir] [train_data] [dev_data] [--n-iter] [--parser-L1] [--no-tagger] [--no-parser] [--no-ner]
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python -m spacy train [lang] [output_dir] [train_data] [dev_data] [--n-iter] [--n-sents] [--use-gpu] [--no-tagger] [--no-parser] [--no-entities]
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+table(["Argument", "Type", "Description"])
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+row
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@ -192,18 +192,13 @@ p
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+row
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+cell #[code --n-iter], #[code -n]
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+cell option
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+cell Number of iterations (default: #[code 15]).
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+cell Number of iterations (default: #[code 20]).
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+row
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+cell #[code --n_sents], #[code -ns]
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+cell #[code --n-sents], #[code -ns]
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+cell option
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+cell Number of sentences (default: #[code 0]).
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+row
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+cell #[code --parser-L1], #[code -L]
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+cell option
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+cell L1 regularization penalty for parser (default: #[code 0.0]).
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+row
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+cell #[code --use-gpu], #[code -G]
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+cell flag
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@ -220,7 +215,7 @@ p
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+cell Don't train parser.
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+row
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+cell #[code --no-ner], #[code -N]
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+cell #[code --no-entities], #[code -N]
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+cell flag
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+cell Don't train NER.
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@ -229,6 +224,106 @@ p
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+cell flag
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+cell Show help message and available arguments.
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+h(3, "train-hyperparams") Environment variables for hyperparameters
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p
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| spaCy lets you set hyperparameters for training via environment variables.
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| This is useful, because it keeps the command simple and allows you to
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| #[+a("https://askubuntu.com/questions/17536/how-do-i-create-a-permanent-bash-alias/17537#17537") create an alias]
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| for your custom #[code train] command while still being able to easily
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| tweak the hyperparameters. For example:
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+code(false, "bash").
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parser_hidden_depth=2 parser_maxout_pieces=2 train-parser
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+under-construction
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+table(["Name", "Description", "Default"])
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+row
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+cell #[code dropout_from]
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+cell
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+cell #[code 0.2]
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+row
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+cell #[code dropout_to]
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+cell
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+cell #[code 0.2]
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+row
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+cell #[code dropout_decay]
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+cell
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+cell #[code 0.0]
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+row
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+cell #[code batch_from]
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+cell
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+cell #[code 1]
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+row
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+cell #[code batch_to]
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+cell
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+cell #[code 64]
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+row
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+cell #[code batch_compound]
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+cell
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+cell #[code 1.001]
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+row
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+cell #[code token_vector_width]
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+cell
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+cell #[code 128]
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+row
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+cell #[code embed_size]
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+cell
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+cell #[code 7500]
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+row
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+cell #[code parser_maxout_pieces]
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+cell
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+cell #[code ]
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+row
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+cell #[code parser_hidden_depth]
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+cell
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+cell #[code ]
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+row
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+cell #[code hidden_width]
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+cell
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+cell #[code 128]
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+row
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+cell #[code learn_rate]
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+cell
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+cell #[code 0.001]
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+row
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+cell #[code optimizer_B1]
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+cell
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+cell #[code 0.9]
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+row
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+cell #[code optimizer_B2]
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+cell
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+cell #[code 0.999]
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+row
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+cell #[code optimizer_eps]
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+cell
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+cell #[code 1e-08]
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+row
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+cell #[code L2_penalty]
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+cell
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+cell #[code 1e-06]
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+row
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+cell #[code grad_norm_clip]
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+cell
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+cell #[code 1.0]
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+h(2, "package") Package
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p
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@ -661,4 +661,4 @@ p
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| model use the using spaCy's #[+api("cli#train") #[code train]] command:
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+code(false, "bash").
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python -m spacy train [lang] [output_dir] [train_data] [dev_data] [--n_iter] [--parser_L1] [--no_tagger] [--no_parser] [--no_ner]
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python -m spacy train [lang] [output_dir] [train_data] [dev_data] [--n-iter] [--n-sents] [--use-gpu] [--no-tagger] [--no-parser] [--no-entities]
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