Mask illegal langauges by setting them to -ninf. 0 means that illega…

stanza/models/langid/model.py

@@ -58,11 +58,15 @@ def __init__(self, char_to_idx, tag_to_idx, num_layers, embedding_dim, hidden_di
     def build_lang_mask(self, use_gpu=None):
         """
         Build language mask if a lang subset is specified (e.g. ["en", "fr"])
+
+        The mask will be added to the results to set the prediction scores of illegal languages to -inf
         """
         device = torch.device("cuda") if use_gpu else None
-        lang_mask_list = [int(lang in self.lang_subset) for lang in self.idx_to_tag] if self.lang_subset else \
-                         [1 for lang in self.idx_to_tag]
-        self.lang_mask = torch.tensor(lang_mask_list, device=device, dtype=torch.float)
+        if self.lang_subset:
+            lang_mask_list = [0.0 if lang in self.lang_subset else -float('inf') for lang in self.idx_to_tag]
+            self.lang_mask = torch.tensor(lang_mask_list, device=device, dtype=torch.float)
+        else:
+            self.lang_mask = torch.zeros(len(self.idx_to_tag), device=device, dtype=torch.float)
 
     def loss(self, Y_hat, Y):
         return self.loss_train(Y_hat, Y)
@@ -87,7 +91,7 @@ def prediction_scores(self, x):
         if self.lang_subset:
             prediction_batch_size = prediction_probs.size()[0]
             batch_mask = torch.stack([self.lang_mask for _ in range(prediction_batch_size)])
-            prediction_probs = prediction_probs * batch_mask
+            prediction_probs = prediction_probs + batch_mask
         return torch.argmax(prediction_probs, dim=1)
 
     def save(self, path):

stanza/tests/langid/test_langid.py

@@ -578,6 +578,23 @@ def test_lang_subset():
     nlp(docs)
     assert [doc.lang for doc in docs] == ["en", "en"]
 
+def test_lang_subset_unlikely_language():
+    """
+    Test that the language subset masking chooses a legal language, even if all legal languages are supa unlikely
+    """
+    sentences = ["你好" * 200]
+    docs = [Document([], text=text) for text in sentences]
+    nlp = Pipeline(dir=TEST_MODELS_DIR, lang="multilingual", processors="langid", langid_lang_subset=["en"])
+    nlp(docs)
+    assert [doc.lang for doc in docs] == ["en"]
+
+    processor = nlp.processors['langid']
+    model = processor._model
+    text_tensor = processor._text_to_tensor(sentences)
+    en_idx = model.tag_to_idx['en']
+    predictions = model(text_tensor)
+    assert predictions[0, en_idx] < 0, "If this test fails, then regardless of how unlikely it was, the model is predicting the input string is possibly English.  Update the test by picking a different combination of languages & input"
+
 def test_multilingual_pipeline():
     """
     Basic test of multilingual pipeline