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/rag/distributed_retriever.py

@@ -16,7 +16,7 @@ logger = logging.getLogger(__name__)
 class RagPyTorchDistributedRetriever(RagRetriever):
     """
     A distributed retriever built on top of the ``torch.distributed`` communication package. During training all workers
-    initalize their own instance of the retriever, however, only the main worker loads the index into memory. The index is stored
+    initialize their own instance of the retriever, however, only the main worker loads the index into memory. The index is stored
     in cpu memory. The index will also work well in a non-distributed setup.
 
     Args:
@@ -45,7 +45,7 @@ class RagPyTorchDistributedRetriever(RagRetriever):
 
     def init_retrieval(self, distributed_port: int):
         """
-        Retriever initalization function, needs to be called from the training process. The function sets some common parameters
+        Retriever initialization function, needs to be called from the training process. The function sets some common parameters
         and environment variables. On top of that, (only) the main process in the process group loads the index into memory.
 
         Args:
@@ -56,7 +56,7 @@ class RagPyTorchDistributedRetriever(RagRetriever):
 
         logger.info("initializing retrieval")
 
-        # initializing a separate process group for retrievel as the default
+        # initializing a separate process group for retrieval as the default
         # nccl backend doesn't support gather/scatter operations while gloo
         # is too slow to replace nccl for the core gpu communication
         if dist.is_initialized():
@@ -101,7 +101,7 @@ class RagPyTorchDistributedRetriever(RagRetriever):
             n_docs (:obj:`int`):
                 The number of docs retrieved per query.
 
-        Ouput:
+        Output:
             retrieved_doc_embeds (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs, dim)`
                 The retrieval embeddings of the retrieved docs per query.
             doc_ids (:obj:`np.ndarray` of shape :obj:`batch_size, n_docs`)

examples/rag/eval_rag.py

@@ -176,7 +176,7 @@ def get_args():
         choices=["e2e", "retrieval"],
         default="e2e",
         type=str,
-        help="Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calulates precision@k.",
+        help="Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates precision@k.",
     )
     parser.add_argument("--k", default=1, type=int, help="k for the precision@k calculation")
     parser.add_argument(
@@ -206,7 +206,7 @@ def get_args():
         "--predictions_path",
         type=str,
         default="predictions.txt",
-        help="Name of the predictions file, to be stored in the checkpoints directry",
+        help="Name of the predictions file, to be stored in the checkpoints directory",
     )
     parser.add_argument(
         "--eval_all_checkpoints",

examples/rag/use_own_knowledge_dataset.py

@@ -26,7 +26,7 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 
 
 def split_text(text: str, n=100, character=" ") -> List[str]:
-    """Split the text every ``n``-th occurence of ``character``"""
+    """Split the text every ``n``-th occurrence of ``character``"""
     text = text.split(character)
     return [character.join(text[i : i + n]).strip() for i in range(0, len(text), n)]