Validate other languages

ulmfit/train_xlingual_clas.py

@@ -19,7 +19,7 @@ class XLingualCLSHyperParams(CLSHyperParams):
         super().__post_init__(*args, **kwargs)
         self.target_paths = [] if self.target_paths is None else self.target_paths
 
-    def load_cls_data(self, bs, force=False, use_test_for_validation=False, **kwargs):
+    def get_tokenizer_args(self):
         if self.tokenizer is Tokenizers.SUBWORD:
             args = get_sentencepiece(self.base_lm_path.parent, self.base_lm_path.parent / 'train.csv',
                                      self.name, vocab_size=self.max_vocab, pre_rules=[], post_rules=[])
@@ -33,6 +33,10 @@ class XLingualCLSHyperParams(CLSHyperParams):
             raise ValueError(
                 f"self.tokenizer has wrong value {self.tokenizer}, Allowed values are taken from {Tokenizers}")
 
+        return args
+
+    def load_cls_data(self, bs, force=False, use_test_for_validation=False, **kwargs):
+        args = self.get_tokenizer_args()
         src_path = self.dataset_path
         csv_name = self.csv_name
         tgt_paths = [Path(tgt_path) for tgt_path in self.target_paths]
@@ -42,11 +46,6 @@ class XLingualCLSHyperParams(CLSHyperParams):
 
         xcvs_name = ('x_' + csv_name)
         mixed_csv.to_csv(src_path / xcvs_name, header=None, index=False)
-        
-        data_eval = [
-            TextClasDataBunch.from_csv(path=tgt_path, csv_name=csv_name, **kwargs)
-            for tgt_path in tgt_paths
-        ]
 
         try:
             if force: raise FileNotFoundError("Forcing reloading of caches")
@@ -72,7 +71,20 @@ class XLingualCLSHyperParams(CLSHyperParams):
 
         print('Size of vocabulary:', len(data_lm.vocab.itos))
         print('First 20 words in vocab:', data_lm.vocab.itos[:20])
-        return data_cls, data_lm # , data_eval
+        return data_cls, data_lm
+
+    def validate_cls(self, save_name='cls_last', bs=40):
+        args = self.get_tokenizer_args()
+        data_clas, data_lm = self.load_cls_data_full(bs, use_test_for_validation=True)
+        data_eval = [
+            TextClasDataBunch.from_csv(path=Path(tgt_path), csv_name=self.csv_name, **args)
+            for tgt_path in self.target_paths
+        ]
+
+        for data in [data_clas] + data_eval:
+            learn = self.create_cls_learner(data, drop_mult=0.1)
+            learn.load(save_name)
+            print(f"Loss and accuracy using ({save_name}) for dataset at {data.path}:", learn.validate())
 
 
 if __name__ == '__main__':