SqueezeBERT architecture (#7083)

* configuration_squeezebert.py

thin wrapper around bert tokenizer

fix typos

wip sb model code

wip modeling_squeezebert.py. Next step is to get the multi-layer-output interface working

set up squeezebert to use BertModelOutput when returning results.

squeezebert documentation

formatting

allow head mask that is an array of [None, ..., None]

docs

docs cont'd

path to vocab

docs and pointers to cloud files (WIP)

line length and indentation

squeezebert model cards

formatting of model cards

untrack modeling_squeezebert_scratchpad.py

update aws paths to vocab and config files

get rid of stub of NSP code, and advise users to pretrain with mlm only

fix rebase issues

redo rebase of modeling_auto.py

fix issues with code formatting

more code format auto-fixes

move squeezebert before bert in tokenization_auto.py and modeling_auto.py because squeezebert inherits from bert

tests for squeezebert modeling and tokenization

fix typo

move squeezebert before bert in modeling_auto.py to fix inheritance problem

disable test_head_masking, since squeezebert doesn't yet implement head masking

fix issues exposed by the test_modeling_squeezebert.py

fix an issue exposed by test_tokenization_squeezebert.py

fix issue exposed by test_modeling_squeezebert.py

auto generated code style improvement

issue that we inherited from modeling_xxx.py: SqueezeBertForMaskedLM.forward() calls self.cls(), but there is no self.cls, and I think the goal was actually to call self.lm_head()

update copyright

resolve failing 'test_hidden_states_output' and remove unused encoder_hidden_states and encoder_attention_mask

docs

add integration test. rename squeezebert-mnli --> squeezebert/squeezebert-mnli

autogenerated formatting tweaks

integrate feedback from patrickvonplaten and sgugger to programming style and documentation strings

* tiny change to order of imports

docs/source/index.rst

@@ -148,7 +148,10 @@ conversion utilities for the following models:
 29. `XLNet <https://github.com/zihangdai/xlnet>`_ (from Google/CMU) released with the paper `​XLNet: Generalized
     Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang, Zihang
     Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V. Le.
-30. `Other community models <https://huggingface.co/models>`_, contributed by the `community
+30. SqueezeBERT (from UC Berkeley) released with the paper
+    `SqueezeBERT: What can computer vision teach NLP about efficient neural networks? <https://arxiv.org/abs/2006.11316>`_
+    by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
+31. `Other community models <https://huggingface.co/models>`_, contributed by the `community
     <https://huggingface.co/users>`_.
 
 .. toctree::
@@ -241,6 +244,7 @@ conversion utilities for the following models:
     model_doc/reformer
     model_doc/retribert
     model_doc/roberta
+    model_doc/squeezebert
     model_doc/t5
     model_doc/transformerxl
     model_doc/xlm