Correct line number in README for classes

README.md

@@ -85,28 +85,28 @@ python -m pytest -sv tests/
 This package comprises the following classes that can be imported in Python and are detailed in the [Doc](#doc) section of this readme:
 
 - Eight **Bert** PyTorch models (`torch.nn.Module`) with pre-trained weights (in the [`modeling.py`](./pytorch_pretrained_bert/modeling.py) file):
-  - [`BertModel`](./pytorch_pretrained_bert/modeling.py#L556) - raw BERT Transformer model (**fully pre-trained**),
-  - [`BertForMaskedLM`](./pytorch_pretrained_bert/modeling.py#L710) - BERT Transformer with the pre-trained masked language modeling head on top (**fully pre-trained**),
-  - [`BertForNextSentencePrediction`](./pytorch_pretrained_bert/modeling.py#L771) - BERT Transformer with the pre-trained next sentence prediction classifier on top  (**fully pre-trained**),
-  - [`BertForPreTraining`](./pytorch_pretrained_bert/modeling.py#L639) - BERT Transformer with masked language modeling head and next sentence prediction classifier on top (**fully pre-trained**),
-  - [`BertForSequenceClassification`](./pytorch_pretrained_bert/modeling.py#L833) - BERT Transformer with a sequence classification head on top (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**),
-  - [`BertForMultipleChoice`](./pytorch_pretrained_bert/modeling.py#L899) - BERT Transformer with a multiple choice head on top (used for task like Swag) (BERT Transformer is **pre-trained**, the multiple choice classification head **is only initialized and has to be trained**),
-  - [`BertForTokenClassification`](./pytorch_pretrained_bert/modeling.py#L969) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**),
-  - [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L1034) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**).
+  - [`BertModel`](./pytorch_pretrained_bert/modeling.py#L639) - raw BERT Transformer model (**fully pre-trained**),
+  - [`BertForMaskedLM`](./pytorch_pretrained_bert/modeling.py#L793) - BERT Transformer with the pre-trained masked language modeling head on top (**fully pre-trained**),
+  - [`BertForNextSentencePrediction`](./pytorch_pretrained_bert/modeling.py#L854) - BERT Transformer with the pre-trained next sentence prediction classifier on top  (**fully pre-trained**),
+  - [`BertForPreTraining`](./pytorch_pretrained_bert/modeling.py#L722) - BERT Transformer with masked language modeling head and next sentence prediction classifier on top (**fully pre-trained**),
+  - [`BertForSequenceClassification`](./pytorch_pretrained_bert/modeling.py#L916) - BERT Transformer with a sequence classification head on top (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**),
+  - [`BertForMultipleChoice`](./pytorch_pretrained_bert/modeling.py#L982) - BERT Transformer with a multiple choice head on top (used for task like Swag) (BERT Transformer is **pre-trained**, the multiple choice classification head **is only initialized and has to be trained**),
+  - [`BertForTokenClassification`](./pytorch_pretrained_bert/modeling.py#L1051) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**),
+  - [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L1124) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**).
 
 - Three **OpenAI GPT** PyTorch models (`torch.nn.Module`) with pre-trained weights (in the [`modeling_openai.py`](./pytorch_pretrained_bert/modeling_openai.py) file):
-  - [`OpenAIGPTModel`](./pytorch_pretrained_bert/modeling_openai.py#L537) - raw OpenAI GPT Transformer model (**fully pre-trained**),
-  - [`OpenAIGPTLMHeadModel`](./pytorch_pretrained_bert/modeling_openai.py#L691) - OpenAI GPT Transformer with the tied language modeling head on top (**fully pre-trained**),
-  - [`OpenAIGPTDoubleHeadsModel`](./pytorch_pretrained_bert/modeling_openai.py#L752) - OpenAI GPT Transformer with the tied language modeling head and a multiple choice classification head on top (OpenAI GPT Transformer is **pre-trained**, the multiple choice classification head **is only initialized and has to be trained**),
+  - [`OpenAIGPTModel`](./pytorch_pretrained_bert/modeling_openai.py#L536) - raw OpenAI GPT Transformer model (**fully pre-trained**),
+  - [`OpenAIGPTLMHeadModel`](./pytorch_pretrained_bert/modeling_openai.py#L643) - OpenAI GPT Transformer with the tied language modeling head on top (**fully pre-trained**),
+  - [`OpenAIGPTDoubleHeadsModel`](./pytorch_pretrained_bert/modeling_openai.py#L722) - OpenAI GPT Transformer with the tied language modeling head and a multiple choice classification head on top (OpenAI GPT Transformer is **pre-trained**, the multiple choice classification head **is only initialized and has to be trained**),
 
 - Two **Transformer-XL** PyTorch models (`torch.nn.Module`) with pre-trained weights (in the [`modeling_transfo_xl.py`](./pytorch_pretrained_bert/modeling_transfo_xl.py) file):
-  - [`TransfoXLModel`](./pytorch_pretrained_bert/modeling_transfo_xl.py#L974) - Transformer-XL model which outputs the last hidden state and memory cells (**fully pre-trained**),
-  - [`TransfoXLLMHeadModel`](./pytorch_pretrained_bert/modeling_transfo_xl.py#L1236) - Transformer-XL with the tied adaptive softmax head on top for language modeling which outputs the logits/loss and memory cells (**fully pre-trained**),
+  - [`TransfoXLModel`](./pytorch_pretrained_bert/modeling_transfo_xl.py#L983) - Transformer-XL model which outputs the last hidden state and memory cells (**fully pre-trained**),
+  - [`TransfoXLLMHeadModel`](./pytorch_pretrained_bert/modeling_transfo_xl.py#L1260) - Transformer-XL with the tied adaptive softmax head on top for language modeling which outputs the logits/loss and memory cells (**fully pre-trained**),
 
 - Three **OpenAI GPT-2** PyTorch models (`torch.nn.Module`) with pre-trained weights (in the [`modeling_gpt2.py`](./pytorch_pretrained_bert/modeling_gpt2.py) file):
-  - [`GPT2Model`](./pytorch_pretrained_bert/modeling_gpt2.py#L537) - raw OpenAI GPT-2 Transformer model (**fully pre-trained**),
-  - [`GPT2LMHeadModel`](./pytorch_pretrained_bert/modeling_gpt2.py#L691) - OpenAI GPT-2 Transformer with the tied language modeling head on top (**fully pre-trained**),
-  - [`GPT2DoubleHeadsModel`](./pytorch_pretrained_bert/modeling_gpt2.py#L752) - OpenAI GPT-2 Transformer with the tied language modeling head and a multiple choice classification head on top (OpenAI GPT-2 Transformer is **pre-trained**, the multiple choice classification head **is only initialized and has to be trained**),
+  - [`GPT2Model`](./pytorch_pretrained_bert/modeling_gpt2.py#L479) - raw OpenAI GPT-2 Transformer model (**fully pre-trained**),
+  - [`GPT2LMHeadModel`](./pytorch_pretrained_bert/modeling_gpt2.py#L559) - OpenAI GPT-2 Transformer with the tied language modeling head on top (**fully pre-trained**),
+  - [`GPT2DoubleHeadsModel`](./pytorch_pretrained_bert/modeling_gpt2.py#L624) - OpenAI GPT-2 Transformer with the tied language modeling head and a multiple choice classification head on top (OpenAI GPT-2 Transformer is **pre-trained**, the multiple choice classification head **is only initialized and has to be trained**),
 
 - Tokenizers for **BERT** (using word-piece) (in the [`tokenization.py`](./pytorch_pretrained_bert/tokenization.py) file):
   - `BasicTokenizer` - basic tokenization (punctuation splitting, lower casing, etc.),