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This is a textcat classifier that pools the vectors generated by a tok2vec implementation and then applies a classifier to the pooled representation. Three reductions are supported for pooling: first, max, and mean. When multiple reductions are enabled, the reductions are concatenated before providing them to the classification layer. This model is a generalization of the TextCatCNN model, which only supports mean reductions and is a bit of a misnomer, because it can also be used with transformers. This change also reimplements TextCatCNN.v2 using the new TextCatReduce.v1 layer. |
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| .. | ||
| models | ||
| __init__.py | ||
| _character_embed.py | ||
| _precomputable_affine.py | ||
| callbacks.py | ||
| extract_ngrams.py | ||
| extract_spans.py | ||
| featureextractor.py | ||
| parser_model.pxd | ||
| parser_model.pyx | ||
| staticvectors.py | ||
| tb_framework.py | ||