Add instructions to load RoBERTa models on PyTorch 1.0

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/921

Differential Revision: D16541025

Pulled By: myleott

fbshipit-source-id: bb78d30fe285da2adfc7c4e5897ee01fa413b2e4

examples/roberta/README.md

@@ -14,15 +14,47 @@ Model | Description | # params | Download
 `roberta.large` | RoBERTa using the BERT-large architecture | 355M | [roberta.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz)
 `roberta.large.mnli` | `roberta.large` finetuned on MNLI | 355M | [roberta.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz)
 
-## Example usage (torch.hub)
+## Results
+
+##### Results on GLUE tasks (dev set, single model, single-task finetuning)
 
-##### Load RoBERTa:
+Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B
+---|---|---|---|---|---|---|---|---
+`roberta.base` | 87.6 | 92.8 | 91.9 | 78.7 | 94.8 | 90.2 | 63.6 | 91.2
+`roberta.large` | 90.2 | 94.7 | 92.2 | 86.6 | 96.4 | 90.9 | 68.0 | 92.4
+`roberta.large.mnli` | 90.2 | - | - | - | - | - | - | -
+
+##### Results on SQuAD (dev set)
+
+Model | SQuAD 1.1 EM/F1 | SQuAD 2.0 EM/F1
+---|---|---
+`roberta.large` | 88.9/94.6 | 86.5/89.4
+
+##### Results on Reading Comprehension (RACE, test set)
+
+Model | Accuracy | Middle | High
+---|---|---|---
+`roberta.large` | 83.2 | 86.5 | 81.3
+
+## Example usage
+
+##### Load RoBERTa from torch.hub (PyTorch >= 1.1):
 ```
 >>> import torch
 >>> roberta = torch.hub.load('pytorch/fairseq', 'roberta.large')
 >>> roberta.eval()  # disable dropout (or leave in train mode to finetune)
 ```
 
+##### Load RoBERTa (for PyTorch 1.0):
+```
+$ wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
+$ tar -xzvf roberta.large.tar.gz
+
+>>> from fairseq.models.roberta import RobertaModel
+>>> roberta = RobertaModel.from_pretrained('/path/to/roberta.large')
+>>> roberta.eval()  # disable dropout (or leave in train mode to finetune)
+```
+
 ##### Apply Byte-Pair Encoding (BPE) to input text:
 ```
 >>> tokens = roberta.encode('Hello world!')
@@ -80,29 +112,7 @@ tensor([[-1.1050, -1.0672, -1.1245]], grad_fn=<LogSoftmaxBackward>)
 tensor([[-1.1050, -1.0672, -1.1245]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)
 ```
 
-## Results
-
-##### Results on GLUE tasks (dev set, single model, single-task finetuning)
-
-Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B
----|---|---|---|---|---|---|---|---
-`roberta.base` | 87.6 | 92.8 | 91.9 | 78.7 | 94.8 | 90.2 | 63.6 | 91.2
-`roberta.large` | 90.2 | 94.7 | 92.2 | 86.6 | 96.4 | 90.9 | 68.0 | 92.4
-`roberta.large.mnli` | 90.2 | - | - | - | - | - | - | -
-
-##### Results on SQuAD (dev set)
-
-Model | SQuAD 1.1 EM/F1 | SQuAD 2.0 EM/F1
----|---|---
-`roberta.large` | 88.9/94.6 | 86.5/89.4
-
-##### Results on Reading Comprehension (RACE, test set)
-
-Model | Accuracy | Middle | High
----|---|---|---
-`roberta.large` | 83.2 | 86.5 | 81.3
-
-## Evaluating the `roberta.large.mnli` model
+##### Evaluating the `roberta.large.mnli` model
 
 Example python code snippet to evaluate accuracy on the MNLI dev_matched set.
 ```