flair workaround

textattack/constraints/grammaticality/part_of_speech.py

@@ -12,6 +12,34 @@ from textattack.shared.validators import transformation_consists_of_word_swaps
 flair.device = textattack.shared.utils.device
 
 
+def load_flair_upos_fast():
+    """Loads flair 'upos-fast' SequenceTagger.
+
+    This is a temporary workaround for flair v0.6. Will be fixed when
+    flair pushes the bug fix.
+    """
+    import pathlib
+    import warnings
+
+    from flair import file_utils
+    import torch
+
+    hu_path: str = "https://nlp.informatik.hu-berlin.de/resources/models"
+    upos_path = "/".join([hu_path, "upos-fast", "en-upos-ontonotes-fast-v0.4.pt"])
+    model_path = file_utils.cached_path(upos_path, cache_dir=pathlib.Path("models"))
+    model_file = SequenceTagger._fetch_model(model_path)
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore")
+        # load_big_file is a workaround by https://github.com/highway11git to load models on some Mac/Windows setups
+        # see https://github.com/zalandoresearch/flair/issues/351
+        f = file_utils.load_big_file(str(model_file))
+        state = torch.load(f, map_location="cpu")
+    model = SequenceTagger._init_model_with_state_dict(state)
+    model.eval()
+    model.to(textattack.shared.utils.device)
+    return model
+
+
 class PartOfSpeech(Constraint):
     """Constraints word swaps to only swap words with the same part of speech.
     Uses the NLTK universal part-of-speech tagger by default. An implementation
@@ -43,7 +71,7 @@ class PartOfSpeech(Constraint):
         self._pos_tag_cache = lru.LRU(2 ** 14)
         if tagger_type == "flair":
             if tagset == "universal":
-                self._flair_pos_tagger = SequenceTagger.load("upos-fast")
+                self._flair_pos_tagger = load_flair_upos_fast()
             else:
                 self._flair_pos_tagger = SequenceTagger.load("pos-fast")
 

textattack/constraints/semantics/bert_score.py

@@ -7,6 +7,8 @@ To decide which two tokens to compare, it greedily chooses the most
 similar token from one text and matches it to a token in the second
 text.
 """
+import warnings
+
 import bert_score
 
 from textattack.constraints import Constraint
@@ -55,7 +57,11 @@ class BERTScore(Constraint):
         `reference_text` is lower than minimum BERT Score."""
         cand = transformed_text.text
         ref = reference_text.text
-        result = self._bert_scorer.score([cand], [ref])
+        with warnings.catch_warnings():
+            # Catch the many warnings that the huggingface API throws when using
+            # BERT scorer.
+            warnings.filterwarnings("ignore")
+            result = self._bert_scorer.score([cand], [ref])
         score = result[BERTScore.SCORE_TYPE2IDX[self.score_type]].item()
         if score >= self.min_bert_score:
             return True