Demo

app.py

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+import gradio as gr
+import torch
+import transformers
+
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+class Interactive:
+    def __init__(self):
+        self.tokenizer = transformers.AutoTokenizer.from_pretrained('liujch1998/cd-pi')
+        self.model = transformers.AutoModelForSeq2SeqLM.from_pretrained('liujch1998/cd-pi').to(device)
+        self.linear = torch.nn.Linear(self.model.shared.embedding_dim, 1).to(device)
+        self.linear.weight = torch.nn.Parameter(self.model.shared.weight[32099, :].unsqueeze(0)) # (1, D)
+        self.linear.bias = torch.nn.Parameter(self.model.shared.weight[32098, 0].unsqueeze(0)) # (1)
+        self.model.eval()
+        self.t = 2.2247
+
+    def run(self, statement):
+        input_ids = self.tokenizer.batch_encode_plus([statement], return_tensors='pt', padding='longest').input_ids.to(device)
+        with torch.no_grad():
+            output = self.model(input_ids)
+            last_hidden_state = output.last_hidden_state.to(device) # (B=1, L, D)
+            hidden = last_hidden_state[0, -1, :] # (D)
+            logit = self.linear(hidden).squeeze(-1) # ()
+            logit_calibrated = logit / self.t
+            score = logit.sigmoid()
+            score_calibrated = logit_calibrated.sigmoid()
+        return {
+            'logit': logit.item(),
+            'logit_calibrated': logit_calibrated.item(),
+            'score': score.item(),
+            'score_calibrated': score_calibrated.item(),
+        }
+
+interactive = Interactive()
+
+def predict(statement, model):
+    result = interactive.run(statement)
+    return {
+        'True': result['score_calibrated'],
+        'False': 1 - result['score_calibrated'],
+    }
+
+examples = [
+    'If A sits next to B and B sits next to C, then A must sit next to C.',
+    'If A sits next to B and B sits next to C, then A might not sit next to C.',
+]
+
+input_statement = gr.Dropdown(choices=examples, label='Statement:')
+input_model = gr.Textbox(label='Commonsense statement verification model:', value='liujch1998/cd-pi', interactive=False)
+output = gr.outputs.Label(num_top_classes=2)
+
+description = '''This is a demo for a commonsense statement verification model. Under development.'''
+
+gr.Interface(
+    fn=predict,
+    inputs=[input_statement, input_model],
+    outputs=output,
+    title="cd-pi Demo",
+    description=description,
+).launch()

requirements.txt

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+torch
+transformers
+tokenizers
+sentencepiece