Remove constraint_type parameter from CRF Tagger (allenai#2208)

WrRan · Dec 19, 2018 · 4cc4b6b · 4cc4b6b
1 parent eff25a3
commit 4cc4b6b
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Showing 9 changed files with 4 additions and 59 deletions.
diff --git a/allennlp/models/crf_tagger.py b/allennlp/models/crf_tagger.py
@@ -1,5 +1,4 @@
 from typing import Dict, Optional, List, Any
-import warnings
 
 from overrides import overrides
 import torch
@@ -39,17 +38,6 @@ class CrfTagger(Model):
         Label encoding to use when calculating span f1 and constraining
         the CRF at decoding time . Valid options are "BIO", "BIOUL", "IOB1", "BMES".
         Required if ``calculate_span_f1`` or ``constrain_crf_decoding`` is true.
-    constraint_type : ``str``, optional (default=``None``)
-        If provided, the CRF will be constrained at decoding time
-        to produce valid labels based on the specified type
-        (e.g. "BIO", or "BIOUL").
-
-        .. deprecated:: 0.6.1
-           ``constraint_type`` was deprecated and replaced with
-           ``label_encoding``, ``constrain_crf_decoding``, and
-           ``calculate_span_f1`` in version 0.6.1. It will be removed
-           in version 0.8.
-
     include_start_end_transitions : ``bool``, optional (default=``True``)
         Whether to include start and end transition parameters in the CRF.
     constrain_crf_decoding : ``bool``, optional (default=``None``)
@@ -81,7 +69,6 @@ def __init__(self, vocab: Vocabulary,
                  label_namespace: str = "labels",
                  feedforward: Optional[FeedForward] = None,
                  label_encoding: Optional[str] = None,
-                 constraint_type: Optional[str] = None,
                  include_start_end_transitions: bool = True,
                  constrain_crf_decoding: bool = None,
                  calculate_span_f1: bool = None,
@@ -109,13 +96,6 @@ def __init__(self, vocab: Vocabulary,
         self.tag_projection_layer = TimeDistributed(Linear(output_dim,
                                                            self.num_tags))
 
-        if constraint_type is not None:
-            warnings.warn("'constraint_type' was removed and replaced with"
-                          "'label_encoding', 'constrain_crf_decoding', and "
-                          "'calculate_span_f1' in version 0.6.1. It will be "
-                          "removed in version 0.8.", DeprecationWarning)
-            label_encoding = constraint_type
-
         # if  constrain_crf_decoding and calculate_span_f1 are not
         # provided, (i.e., they're None), set them to True
         # if label_encoding is provided and False if it isn't.
@@ -152,11 +132,6 @@ def __init__(self, vocab: Vocabulary,
             self._f1_metric = SpanBasedF1Measure(vocab,
                                                  tag_namespace=label_namespace,
                                                  label_encoding=label_encoding)
-        elif constraint_type is not None:
-            # Maintain deprecated behavior if constraint_type is provided
-            self._f1_metric = SpanBasedF1Measure(vocab,
-                                                 tag_namespace=label_namespace,
-                                                 label_encoding=constraint_type)
 
         check_dimensions_match(text_field_embedder.get_output_dim(), encoder.get_input_dim(),
                                "text field embedding dim", "encoder input dim")

diff --git a/allennlp/pretrained.py b/allennlp/pretrained.py
@@ -47,7 +47,7 @@ def neural_coreference_resolution_lee_2017() -> predictors.CorefPredictor:
     return model.predictor() # type: ignore
 
 def named_entity_recognition_with_elmo_peters_2018() -> predictors.SentenceTaggerPredictor:
-    model = PretrainedModel('https://s3-us-west-2.amazonaws.com/allennlp/models/ner-model-2018.04.30.tar.gz',
+    model = PretrainedModel('https://s3-us-west-2.amazonaws.com/allennlp/models/ner-model-2019.12.18.tar.gz',
                             'sentence-tagger')
     return model.predictor() # type: ignore
 

diff --git a/allennlp/tests/fixtures/semantic_parsing/quarel/serialization_tagger/model.tar.gz b/allennlp/tests/fixtures/semantic_parsing/quarel/serialization_tagger/model.tar.gz
diff --git a/allennlp/tests/fixtures/semantic_parsing/quarel/tagger/experiment.json b/allennlp/tests/fixtures/semantic_parsing/quarel/tagger/experiment.json
@@ -22,7 +22,7 @@
   "validation_data_path": "allennlp/tests/fixtures/data/quarel.jsonl",
   "model": {
     "type": "crf_tagger",
-    "constraint_type": "BIO",
+    "label_encoding": "BIO",
     "dropout": 0.2,
     "include_start_end_transitions": false,
     "text_field_embedder": {

diff --git a/allennlp/tests/fixtures/semantic_parsing/quarel/tagger/serialization/model.tar.gz b/allennlp/tests/fixtures/semantic_parsing/quarel/tagger/serialization/model.tar.gz
diff --git a/allennlp/tests/models/crf_tagger_test.py b/allennlp/tests/models/crf_tagger_test.py
@@ -25,34 +25,6 @@ def test_simple_tagger_can_train_save_and_conll2000(self):
         self.ensure_model_can_train_save_and_load(
                 self.FIXTURES_ROOT / 'crf_tagger' / 'experiment_conll2000.json')
 
-    def test_simple_tagger_constraint_type_deprecated(self):
-        params = Params({"model": {
-                "type": "crf_tagger",
-                "constraint_type": "IOB1",
-                "text_field_embedder": {
-                        "token_embedders": {
-                                "tokens": {
-                                        "type": "embedding",
-                                        "embedding_dim": 50
-                                },
-                        }
-                },
-                "encoder": {
-                        "type": "gru",
-                        "input_size": 50,
-                        "hidden_size": 10,
-                        "num_layers": 2,
-                        "dropout": 0.5,
-                        "bidirectional": True
-                }}})
-        with pytest.warns(DeprecationWarning):
-            model = Model.from_params(vocab=self.vocab,
-                                      params=params.pop("model"))
-        assert model._f1_metric is not None
-        assert model._f1_metric._label_encoding == "IOB1"
-        assert model.label_encoding == "IOB1"
-        assert model.crf._constraint_mask.sum().item() != (model.num_tags + 2)**2
-
     @flaky
     def test_batch_predictions_are_consistent(self):
         self.ensure_batch_predictions_are_consistent()

diff --git a/training_config/ner_elmo.jsonnet b/training_config/ner_elmo.jsonnet
@@ -31,7 +31,7 @@
   "validation_data_path": std.extVar("NER_TEST_A_PATH"),
   "model": {
     "type": "crf_tagger",
-    "constraint_type": "BIOUL",
+    "label_encoding": "BIOUL",
     "dropout": 0.5,
     "include_start_end_transitions": false,
     "text_field_embedder": {

diff --git a/training_config/quarel_tagger.json b/training_config/quarel_tagger.json
@@ -23,7 +23,7 @@
   "validation_data_path": "https://s3-us-west-2.amazonaws.com/allennlp/datasets/quarel/quarel-v1-dev.jsonl",
   "model": {
     "type": "crf_tagger",
-    "constraint_type": "BIO",
+    "label_encoding": "BIO",
     "dropout": 0.2,
     "include_start_end_transitions": false,
     "text_field_embedder": {

diff --git a/tutorials/getting_started/walk_through_allennlp/creating_a_model.md b/tutorials/getting_started/walk_through_allennlp/creating_a_model.md
@@ -177,8 +177,6 @@ We don't *need* to, but we also make a few other changes
   treated as out-of-vocabulary at evaluation time. The second flag just evaluates
   the model on the test set when training stops. Use this flag cautiously,
   when you're doing real science you don't want to evaluate on your test set too often.
-* if you want to specify constraints for the CRF, you can add a `"model.constraint_type"`
-  to your config that indicates what sort of constraints the CRF tagger should use.
 
 
 ## Putting It All Together