Fix many typos (#8708)

docs/source/index.rst

@@ -44,7 +44,7 @@ The documentation is organized in five parts:
   and a glossary.
 - **USING 🤗 TRANSFORMERS** contains general tutorials on how to use the library.
 - **ADVANCED GUIDES** contains more advanced guides that are more specific to a given script or part of the library.
-- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general resarch in
+- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general research in
   transformers model
 - The three last section contain the documentation of each public class and function, grouped in:
 

docs/source/model_doc/dpr.rst

@@ -5,7 +5,7 @@ Overview
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. It was
-intorduced in `Dense Passage Retrieval for Open-Domain Question Answering <https://arxiv.org/abs/2004.04906>`__ by
+introduced in `Dense Passage Retrieval for Open-Domain Question Answering <https://arxiv.org/abs/2004.04906>`__ by
 Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih.
 
 The abstract from the paper is the following:

docs/source/model_summary.rst

@@ -530,7 +530,7 @@ Sequence-to-sequence model with the same encoder-decoder model architecture as B
 two self-supervised objective functions: Masked Language Modeling (MLM) and a novel summarization specific pre-training
 objective, called Gap Sentence Generation (GSG).
 
-  * MLM: encoder input tokens are randomely replaced by a mask tokens and have to be predicted by the encoder (like in
+  * MLM: encoder input tokens are randomly replaced by a mask tokens and have to be predicted by the encoder (like in
     BERT)
   * GSG: whole encoder input sentences are replaced by a second mask token and fed to the decoder, but which has a
     causal mask to hide the future words like a regular auto-regressive transformer decoder.

docs/source/multilingual.rst

@@ -109,7 +109,7 @@ XLM-RoBERTa
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 XLM-RoBERTa was trained on 2.5TB of newly created clean CommonCrawl data in 100 languages. It provides strong gains
-over previously released multi-lingual models like mBERT or XLM on downstream taks like classification, sequence
+over previously released multi-lingual models like mBERT or XLM on downstream tasks like classification, sequence
 labeling and question answering.
 
 Two XLM-RoBERTa checkpoints can be used for multi-lingual tasks:

docs/source/perplexity.rst

@@ -62,7 +62,7 @@ sliding the context window so that the model has more context when making each p
 This is a closer approximation to the true decomposition of the sequence probability and will typically yield a more
 favorable score. The downside is that it requires a separate forward pass for each token in the corpus. A good
 practical compromise is to employ a strided sliding window, moving the context by larger strides rather than sliding by
-1 token a time. This allows computation to procede much faster while still giving the model a large context to make
+1 token a time. This allows computation to proceed much faster while still giving the model a large context to make
 predictions at each step.
 
 Example: Calculating perplexity with GPT-2 in 🤗 Transformers