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In the reference implementations, there's usually a function to build a ffnn of arbitrary depth, consisting of a stack of Linear >> Relu >> Dropout. In practice the depth is always 1 in coref-hoi, but in earlier iterations of the model, which are more similar to our model here (since we aren't using attention or even necessarily BERT), using a small depth like 2 was common. This hard-codes a stack of 2. In brief tests this allows similar performance to the unstacked version with much smaller embedding sizes. The depth of the stack could be made into a hyperparameter. |
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| .. | ||
| models | ||
| __init__.py | ||
| _character_embed.py | ||
| _precomputable_affine.py | ||
| extract_ngrams.py | ||
| extract_spans.py | ||
| featureextractor.py | ||
| parser_model.pxd | ||
| parser_model.pyx | ||
| staticvectors.py | ||
| tb_framework.py | ||