added readme code for inference with GLUE finetuned model

Summary: Pull Request resolved: fairinternal/fairseq-py#820

Differential Revision: D16783469

fbshipit-source-id: d5af8ba6a6685608d67b72d584952b8e43eabf9f

examples/roberta/README.finetune_glue.md

@@ -65,3 +65,35 @@ a) `--total-num-updates` is used by `--polynomial_decay` scheduler and is calcul
 b) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--max-sentences`.
 
 c) All the settings in above table are suggested settings based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search.  
+
+### Inference on GLUE task
+After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet:
+
+```python
+from fairseq.models.roberta import RobertaModel
+
+roberta = RobertaModel.from_pretrained(
+    'checkpoints/',
+    checkpoint_file='checkpoint_best.pt',
+    data_name_or_path='RTE-bin'
+)
+
+label_fn = lambda label: roberta.task.label_dictionary.string(
+    [label + roberta.task.target_dictionary.nspecial]
+)
+ncorrect, nsamples = 0, 0
+roberta.cuda()
+roberta.eval()
+with open('glue_data/RTE/dev.tsv') as fin:
+    fin.readline()
+    for index, line in enumerate(fin):
+        tokens = line.strip().split('\t')
+        sent1, sent2, target = tokens[1], tokens[2], tokens[3]
+        tokens = roberta.encode(sent1, sent2)
+        prediction = roberta.predict('sentence_classification_head', tokens).argmax().item()
+        prediction_label = label_fn(prediction)
+        ncorrect += int(prediction_label == target)
+        nsamples += 1
+print('| Accuracy: ', float(ncorrect)/float(nsamples))
+
+```