Fix doc errors and typos across the board (#8139)

* Fix doc errors and typos across the board

* Fix a typo

* Fix the CI

* Fix more typos

* Fix CI

* More fixes

* Fix CI

* More fixes

* More fixes

src/transformers/data/data_collator.py

@@ -330,7 +330,7 @@ class DataCollatorForSOP(DataCollatorForLanguageModeling):
         input_ids, labels, attention_mask = self.mask_tokens(input_ids)
 
         token_type_ids = [example["token_type_ids"] for example in examples]
-        # size of segment_ids varied because randomness, padding zero to the end as the orignal implementation
+        # size of segment_ids varied because randomness, padding zero to the end as the original implementation
         token_type_ids = pad_sequence(token_type_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id)
 
         sop_label_list = [example["sentence_order_label"] for example in examples]

src/transformers/data/datasets/language_modeling.py

@@ -71,7 +71,7 @@ class TextDataset(Dataset):
                         tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size])
                     )
                 # Note that we are losing the last truncated example here for the sake of simplicity (no padding)
-                # If your dataset is small, first you should loook for a bigger one :-) and second you
+                # If your dataset is small, first you should look for a bigger one :-) and second you
                 # can change this behavior by adding (model specific) padding.
 
                 start = time.time()

src/transformers/data/processors/squad.py

@@ -327,7 +327,7 @@ def squad_convert_examples_to_features(
         padding_strategy: Default to "max_length". Which padding strategy to use
         return_dataset: Default False. Either 'pt' or 'tf'.
             if 'pt': returns a torch.data.TensorDataset, if 'tf': returns a tf.data.Dataset
-        threads: multiple processing threadsa-smi
+        threads: multiple processing threads.
 
 
     Returns:
@@ -527,7 +527,7 @@ def squad_convert_examples_to_features(
 
 class SquadProcessor(DataProcessor):
     """
-    Processor for the SQuAD data set. Overriden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and
+    Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and
     version 2.0 of SQuAD, respectively.
     """
 

src/transformers/data/processors/utils.py

@@ -245,9 +245,6 @@ class SingleSentenceClassificationProcessor(DataProcessor):
         Args:
             tokenizer: Instance of a tokenizer that will tokenize the examples
             max_length: Maximum example length
-            task: GLUE task
-            label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method
-            output_mode: String indicating the output mode. Either ``regression`` or ``classification``
             pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
             pad_token: Padding token
             mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values