Updated documentation for LM finetuning script

jozhang97 · Sep 2, 2019 · 7f52243 · 7f52243
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diff --git a/docs/source/examples.rst b/docs/source/examples.rst
@@ -459,14 +459,16 @@ The same option as in the original scripts are provided, please refer to the cod
 
 
 Causal LM fine-tuning on GPT/GPT-2, Masked LM fine-tuning on BERT/RoBERTa
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 Before running the following examples you should download the `WikiText-2 dataset <https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/>`__ and unpack it to some directory `$WIKITEXT_2_DATASET`
 The following results were obtained using the `raw` WikiText-2 (no tokens were replaced before the tokenization).
 
 This example fine-tunes GPT-2 on the WikiText-2 dataset. The loss function is a causal language modeling loss (perplexity).
 
 .. code-block:: bash
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     export WIKITEXT_2_DATASET=/path/to/wikitext_dataset
 
     python run_lm_finetuning.py
@@ -485,6 +487,8 @@ This example fine-tunes RoBERTa on the WikiText-2 dataset. The loss function is
 The `--mlm` flag is necessary to fine-tune BERT/RoBERTa on masked language modeling.
 
 .. code-block:: bash
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     export WIKITEXT_2_DATASET=/path/to/wikitext_dataset
 
     python run_lm_finetuning.py

diff --git a/docs/source/model_doc/distilbert.rst b/docs/source/model_doc/distilbert.rst
@@ -2,35 +2,35 @@ DistilBERT
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 ``DistilBertConfig``
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 .. autoclass:: pytorch_transformers.DistilBertConfig
     :members:
 
 
 ``DistilBertTokenizer``
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 .. autoclass:: pytorch_transformers.DistilBertTokenizer
     :members:
 
 
 ``DistilBertModel``
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 .. autoclass:: pytorch_transformers.DistilBertModel
     :members:
 
 
 ``DistilBertForMaskedLM``
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 .. autoclass:: pytorch_transformers.DistilBertForMaskedLM
     :members:
 
 
 ``DistilBertForSequenceClassification``
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 .. autoclass:: pytorch_transformers.DistilBertForSequenceClassification
     :members: