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Respect batch size in training lm model.
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@@ -228,7 +228,7 @@ class LMHyperParams:
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lm_type=self.lm_type)
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elif self.tokenizer is Tokenizers.MOSES_FA:
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try:
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data_lm = TextLMDataBunch.load(self.cache_dir, '.', lm_type=self.lm_type)
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data_lm = TextLMDataBunch.load(self.cache_dir, '.', lm_type=self.lm_type, bs=self.bs)
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print("Tokenized data loaded")
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except FileNotFoundError:
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print("Running tokenization")
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@@ -238,16 +238,18 @@ class LMHyperParams:
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data_lm = TextLMDataBunch.from_df(path=self.cache_dir, train_df=read_file(trn_path),
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valid_df=read_file(val_path), tokenizer=pretokenized,
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test_df=read_file(tst_path), classes=None, lm_type=self.lm_type,
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max_vocab=self.max_vocab)
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max_vocab=self.max_vocab, bs=self.bs)
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data_lm.save('.')
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elif self.tokenizer is Tokenizers.FASTAI:
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try:
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data_lm = TextLMDataBunch.load(self.cache_dir, '.', lm_type=self.lm_type)
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data_lm = TextLMDataBunch.load(self.cache_dir, '.', lm_type=self.lm_type, bs=self.bs)
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print("Tokenized data loaded")
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except FileNotFoundError:
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print("Running tokenization")
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data_lm = TextLMDataBunch.from_df(path=self.cache_dir, train_df=read_file(trn_path), valid_df=read_file(val_path),
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test_df=read_file(tst_path), classes=None, lm_type=self.lm_type, max_vocab=self.max_vocab,)
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test_df=read_file(tst_path), classes=None, lm_type=self.lm_type,
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max_vocab=self.max_vocab,bs=self.bs,
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)
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data_lm.save('.')
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else:
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raise ValueError(f"self.tokenizer has wrong value {self.tokenizer}, Allowed values are taken from {Tokenizers}")
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