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>
28 lines
743 B
Python
28 lines
743 B
Python
import numpy
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from thinc.api import Model, Unserializable
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def SpacyVectors(vectors) -> Model:
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attrs = {"vectors": Unserializable(vectors)}
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model = Model("spacy_vectors", forward, attrs=attrs)
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return model
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def forward(model, docs, is_train: bool):
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batch = []
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vectors = model.attrs["vectors"].obj
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for doc in docs:
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indices = numpy.zeros((len(doc),), dtype="i")
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for i, word in enumerate(doc):
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if word.orth in vectors.key2row:
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indices[i] = vectors.key2row[word.orth]
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else:
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indices[i] = 0
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batch_vectors = vectors.data[indices]
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batch.append(batch_vectors)
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def backprop(dY):
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return None
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return batch, backprop
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