Add model usage code

README.md

@@ -14,7 +14,7 @@ The model classifies an *appraisal* given a sentence and is trained on [ALOE](ht
 
 **Output:** logits (in order of labels)
 
-**Model architecture**: OpenPrompt+RoBERTa
+**Model architecture**: OpenPrompt_+RoBERTa
 
 **Developed by:** Jiamin Yang
 
@@ -48,8 +48,12 @@ from openprompt.plms import load_plm
 from openprompt.prompts import ManualTemplate
 from openprompt.prompts import ManualVerbalizer
 from openprompt import PromptForClassification
+from openprompt.data_utils import InputExample
+from openprompt import PromptDataLoader
 
-checkpoint_file = 'your_path_to/empathy-appraisal-span.pt'
+
+torch.cuda.set_device(1)
+checkpoint_file = 'upload_version/empathy-appraisal-span.pt'
 
 plm, tokenizer, model_config, WrapperClass = load_plm('roberta', 'roberta-large')
 template_text = 'The sentence {"placeholder":"text_a"} has the label {"mask"}.'
@@ -67,5 +71,34 @@ state_dict = checkpoint['model_state_dict']
 del state_dict['prompt_model.plm.roberta.embeddings.position_ids']
 
 prompt_model.load_state_dict(state_dict)
+
+# use the model
+dataset = [
+    InputExample(
+        guid = 0,
+        text_a = "I am sorry for your loss",
+    ),
+    InputExample(
+        guid = 1,
+        text_a = "It's not your fault",
+    ),
+]
+
+data_loader = PromptDataLoader(dataset=dataset, 
+                template=template, 
+                tokenizer=tokenizer,
+                tokenizer_wrapper_class=WrapperClass,
+                max_seq_length=512,
+                batch_size=2,
+                shuffle=False,
+                teacher_forcing=False,
+                predict_eos_token=False,
+                truncate_method='head')
+prompt_model.eval()
+with torch.no_grad():
+    for batch in data_loader:
+        logits = prompt_model(batch.to('cuda'))
+        preds = torch.argmax(logits, dim = -1)
+        print(preds) #[8, 5]
 ```