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

examples/seq2seq/bertabs/configuration_bertabs.py

@@ -44,7 +44,7 @@ class BertAbsConfig(PretrainedConfig):
         enc_ff_size: int
             The size of the encoder's feed-forward layers.
         enc_dropout: int
-            The dropout probabilitiy for all fully connected layers in the
+            The dropout probability for all fully connected layers in the
             embeddings, layers, pooler and also the attention probabilities in
             the encoder.
         dec_layer: int
@@ -56,7 +56,7 @@ class BertAbsConfig(PretrainedConfig):
         dec_ff_size: int
             The size of the decoder's feed-forward layers.
         dec_dropout: int
-            The dropout probabilitiy for all fully connected layers in the
+            The dropout probability for all fully connected layers in the
             embeddings, layers, pooler and also the attention probabilities in
             the decoder.
     """

examples/seq2seq/bertabs/modeling_bertabs.py

@@ -152,7 +152,7 @@ class TransformerDecoder(nn.Module):
        dropout (float): dropout parameters
        embeddings (:obj:`onmt.modules.Embeddings`):
           embeddings to use, should have positional encodings
-       attn_type (str): if using a seperate copy attention
+       attn_type (str): if using a separate copy attention
     """
 
     def __init__(self, num_layers, d_model, heads, d_ff, dropout, embeddings, vocab_size):
@@ -817,11 +817,7 @@ class Translator(object):
 
         Args:
            batch (:obj:`Batch`): a batch from a dataset object
-           data (:obj:`Dataset`): the dataset object
            fast (bool): enables fast beam search (may not support all features)
-
-        Todo:
-           Shouldn't need the original dataset.
         """
         with torch.no_grad():
             return self._fast_translate_batch(batch, self.max_length, min_length=self.min_length)

examples/seq2seq/convert_model_to_fp16.py

@@ -12,7 +12,7 @@ def convert(src_path: str, map_location: str = "cpu", save_path: Union[str, None
     state_dict = torch.load(src_path, map_location=map_location)
     for k, v in tqdm(state_dict.items()):
         if not isinstance(v, torch.Tensor):
-            raise TypeError("FP16 conversion only works on paths that are saved state dics, like pytorch_model.bin")
+            raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin")
         state_dict[k] = v.half()
     if save_path is None:  # overwrite src_path
         save_path = src_path