💫 Make TextCategorizer default to a simpler, GPU-friendly model (#3038)

Currently the TextCategorizer defaults to a fairly complicated model, designed partly around the active learning requirements of Prodigy. The model's a bit slow, and not very GPU-friendly.

This patch implements a straightforward CNN model that still performs pretty well. The replacement model also makes it easy to use the LMAO pretraining, since most of the parameters are in the CNN.

The replacement model has a flag to specify whether labels are mutually exclusive, which defaults to True. This has been a common problem with the text classifier. We'll also now be able to support adding labels to pretrained models again.

Resolves #2934, #2756, #1798, #1748.

examples/training/train_textcat.py

@@ -44,6 +44,7 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
 
     # add label to text classifier
     textcat.add_label("POSITIVE")
+    textcat.add_label("NEGATIVE")
 
     # load the IMDB dataset
     print("Loading IMDB data...")
@@ -64,7 +65,7 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
         for i in range(n_iter):
             losses = {}
             # batch up the examples using spaCy's minibatch
-            batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
+            batches = minibatch(train_data, size=compounding(4.0, 16.0, 1.001))
             for batch in batches:
                 texts, annotations = zip(*batch)
                 nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)
@@ -106,22 +107,24 @@ def load_data(limit=0, split=0.8):
     random.shuffle(train_data)
     train_data = train_data[-limit:]
     texts, labels = zip(*train_data)
-    cats = [{"POSITIVE": bool(y)} for y in labels]
+    cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in labels]
     split = int(len(train_data) * split)
     return (texts[:split], cats[:split]), (texts[split:], cats[split:])
 
 
 def evaluate(tokenizer, textcat, texts, cats):
     docs = (tokenizer(text) for text in texts)
-    tp = 1e-8  # True positives
+    tp = 0.0  # True positives
     fp = 1e-8  # False positives
     fn = 1e-8  # False negatives
-    tn = 1e-8  # True negatives
+    tn = 0.0  # True negatives
     for i, doc in enumerate(textcat.pipe(docs)):
         gold = cats[i]
         for label, score in doc.cats.items():
             if label not in gold:
                 continue
+            if label == "NEGATIVE":
+                continue
             if score >= 0.5 and gold[label] >= 0.5:
                 tp += 1.0
             elif score >= 0.5 and gold[label] < 0.5:

spacy/_ml.py

@@ -5,7 +5,7 @@ import numpy
 from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu
 from thinc.i2v import HashEmbed, StaticVectors
 from thinc.t2t import ExtractWindow, ParametricAttention
-from thinc.t2v import Pooling, sum_pool
+from thinc.t2v import Pooling, sum_pool, mean_pool
 from thinc.misc import Residual
 from thinc.misc import LayerNorm as LN
 from thinc.misc import FeatureExtracter
@@ -575,6 +575,32 @@ def build_text_classifier(nr_class, width=64, **cfg):
     return model
 
 
+def build_simple_cnn_text_classifier(tok2vec, nr_class, exclusive_classes=True, **cfg):
+    """
+    Build a simple CNN text classifier, given a token-to-vector model as inputs.
+    If exclusive_classes=True, a softmax non-linearity is applied, so that the
+    outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
+    is applied instead, so that outputs are in the range [0, 1].
+    """
+    with Model.define_operators({">>": chain}):
+        if exclusive_classes:
+            output_layer = Softmax(nr_class, tok2vec.nO)
+        else:
+            output_layer = (
+                zero_init(Affine(nr_class, tok2vec.nO))
+                >> logistic
+            )
+        model = (
+            tok2vec
+            >> flatten_add_lengths
+            >> Pooling(mean_pool)
+            >> output_layer
+        )
+    model.tok2vec = chain(tok2vec, flatten)
+    model.nO = nr_class
+    return model
+
+
 @layerize
 def flatten(seqs, drop=0.0):
     ops = Model.ops

spacy/pipeline.pyx

@@ -30,6 +30,7 @@ from .tokens.span import Span
 from .attrs import POS, ID
 from .parts_of_speech import X
 from ._ml import Tok2Vec, build_text_classifier, build_tagger_model
+from ._ml import build_simple_cnn_text_classifier
 from ._ml import link_vectors_to_models, zero_init, flatten
 from ._ml import create_default_optimizer
 from .errors import Errors, TempErrors
@@ -1043,15 +1044,20 @@ class TextCategorizer(Pipe):
 
     @classmethod
     def Model(cls, nr_class, **cfg):
-        return build_text_classifier(nr_class, **cfg)
+        embed_size = util.env_opt("embed_size", 2000)
+        if "token_vector_width" in cfg:
+            token_vector_width = cfg["token_vector_width"]
+        else:
+            token_vector_width = util.env_opt("token_vector_width", 96)
+        tok2vec = Tok2Vec(token_vector_width, embed_size, **cfg)
+        return build_simple_cnn_text_classifier(tok2vec, nr_class, **cfg)
 
     @property
     def tok2vec(self):
         if self.model in (None, True, False):
             return None
         else:
-            return chain(self.model.tok2vec, flatten)
-
+            return self.model.tok2vec
 
     def __init__(self, vocab, model=True, **cfg):
         self.vocab = vocab