mirror of
https://github.com/explosion/spaCy.git
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311133e579
* 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>
37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
import numpy
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from thinc.api import Model
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from ..attrs import LOWER
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def extract_ngrams(ngram_size, attr=LOWER) -> Model:
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model = Model("extract_ngrams", forward)
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model.attrs["ngram_size"] = ngram_size
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model.attrs["attr"] = attr
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return model
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def forward(model, docs, is_train: bool):
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batch_keys = []
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batch_vals = []
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for doc in docs:
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unigrams = model.ops.asarray(doc.to_array([model.attrs["attr"]]))
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ngrams = [unigrams]
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for n in range(2, model.attrs["ngram_size"] + 1):
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ngrams.append(model.ops.ngrams(n, unigrams))
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keys = model.ops.xp.concatenate(ngrams)
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keys, vals = model.ops.xp.unique(keys, return_counts=True)
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batch_keys.append(keys)
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batch_vals.append(vals)
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# The dtype here matches what thinc is expecting -- which differs per
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# platform (by int definition). This should be fixed once the problem
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# is fixed on Thinc's side.
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lengths = model.ops.asarray([arr.shape[0] for arr in batch_keys], dtype=numpy.int_)
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batch_keys = model.ops.xp.concatenate(batch_keys)
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batch_vals = model.ops.asarray(model.ops.xp.concatenate(batch_vals), dtype="f")
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def backprop(dY):
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return []
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return (batch_keys, batch_vals, lengths), backprop
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