Fix typograpical errors (#2438)

src/transformers/modeling_albert.py

@@ -355,7 +355,7 @@ class AlbertTransformer(nn.Module):
 
 class AlbertPreTrainedModel(PreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = AlbertConfig

src/transformers/modeling_bert.py

@@ -521,7 +521,7 @@ class BertPreTrainingHeads(nn.Module):
 
 class BertPreTrainedModel(PreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = BertConfig

src/transformers/modeling_ctrl.py

@@ -163,7 +163,7 @@ class EncoderLayer(torch.nn.Module):
 
 class CTRLPreTrainedModel(PreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = CTRLConfig

src/transformers/modeling_gpt2.py

@@ -240,7 +240,7 @@ class Block(nn.Module):
 
 class GPT2PreTrainedModel(PreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = GPT2Config

src/transformers/modeling_openai.py

@@ -257,7 +257,7 @@ class Block(nn.Module):
 
 class OpenAIGPTPreTrainedModel(PreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = OpenAIGPTConfig

src/transformers/modeling_t5.py

@@ -446,7 +446,7 @@ class T5Block(nn.Module):
 
 class T5PreTrainedModel(PreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = T5Config

src/transformers/modeling_tf_albert.py

@@ -435,7 +435,7 @@ class TFAlbertTransformer(tf.keras.layers.Layer):
 
 class TFAlbertPreTrainedModel(TFPreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = AlbertConfig

src/transformers/modeling_tf_bert.py

@@ -576,7 +576,7 @@ class TFBertMainLayer(tf.keras.layers.Layer):
 
 class TFBertPreTrainedModel(TFPreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = BertConfig

src/transformers/modeling_tf_ctrl.py

@@ -344,7 +344,7 @@ class TFCTRLMainLayer(tf.keras.layers.Layer):
 
 class TFCTRLPreTrainedModel(TFPreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = CTRLConfig

src/transformers/modeling_tf_gpt2.py

@@ -360,7 +360,7 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
 
 class TFGPT2PreTrainedModel(TFPreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = GPT2Config

src/transformers/modeling_tf_openai.py

@@ -346,7 +346,7 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
 
 class TFOpenAIGPTPreTrainedModel(TFPreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = OpenAIGPTConfig

src/transformers/modeling_tf_roberta.py

@@ -98,7 +98,7 @@ class TFRobertaMainLayer(TFBertMainLayer):
 
 class TFRobertaPreTrainedModel(TFPreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = RobertaConfig

src/transformers/modeling_tf_t5.py

@@ -514,7 +514,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
 ####################################################
 class TFT5PreTrainedModel(TFPreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = T5Config

src/transformers/modeling_tf_transfo_xl.py

@@ -622,7 +622,7 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
 
 class TFTransfoXLPreTrainedModel(TFPreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = TransfoXLConfig

src/transformers/modeling_tf_xlm.py

@@ -465,7 +465,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
 
 class TFXLMPreTrainedModel(TFPreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = XLMConfig

src/transformers/modeling_tf_xlnet.py

@@ -686,7 +686,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
 
 class TFXLNetPreTrainedModel(TFPreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = XLNetConfig

src/transformers/modeling_transfo_xl.py

@@ -449,7 +449,7 @@ class AdaptiveEmbedding(nn.Module):
 
 class TransfoXLPreTrainedModel(PreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = TransfoXLConfig

src/transformers/modeling_xlm.py

@@ -213,7 +213,7 @@ class TransformerFFN(nn.Module):
 
 class XLMPreTrainedModel(PreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = XLMConfig

src/transformers/modeling_xlnet.py

@@ -468,7 +468,7 @@ class XLNetLayer(nn.Module):
 
 class XLNetPreTrainedModel(PreTrainedModel):
     """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
+        a simple interface for downloading and loading pretrained models.
     """
 
     config_class = XLNetConfig
@@ -514,7 +514,7 @@ XLNET_START_DOCSTRING = r"""    The XLNet model was proposed in
 
     The specific attention pattern can be controlled at training and test time using the `perm_mask` input.
 
-    Do to the difficulty of training a fully auto-regressive model over various factorization order,
+    Due to the difficulty of training a fully auto-regressive model over various factorization order,
     XLNet is pretrained using only a sub-set of the output tokens as target which are selected
     with the `target_mapping` input.
 

src/transformers/tokenization_utils.py

@@ -39,7 +39,7 @@ TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
 
 class PreTrainedTokenizer(object):
     """ Base class for all tokenizers.
-    Handle all the shared methods for tokenization and special tokens as well as methods dowloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.
+    Handle all the shared methods for tokenization and special tokens as well as methods downloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.
 
     This class also contain the added tokens in a unified way on top of all tokenizers so we don't have to handle the specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...).