Create app.py

app.py

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+import gradio as gr
+
+import torch
+import torch.nn.functional as F
+
+from transformers import XGLMTokenizer, XGLMForCausalLM
+
+tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
+model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
+
+data_samples = {
+    'en': [
+        {
+            "premise": "I wanted to conserve energy.",
+            "choice1": "I swept the floor in the unoccupied room.",
+            "choice2": "I shut off the light in the unoccupied room.",
+            "question": "effect",
+            "label": "1"
+        }
+    ],
+    'zh': [
+        {
+            "premise": "我想节约能源。",
+            "choice1": "我在空着的房间里扫了地板。",
+            "choice2": "我把空房间里的灯关了。",
+            "question": "effect",
+            "label": "1"
+        }
+    ]
+}
+
+def get_logprobs(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids, output_ids = inputs["input_ids"], inputs["input_ids"][:, 1:]
+    outputs = model(**inputs, labels=input_ids)
+    logits = outputs.logits
+    logprobs = torch.gather(F.log_softmax(logits, dim=2), 2, output_ids.unsqueeze(2))
+    return logprobs
+ 
+ 
+# Zero-shot evaluation for the Choice of Plausible Alternatives (COPA) task.
+# A return value of 0 indicates that the first alternative is more plausible,
+# while 1 indicates that the second alternative is more plausible.
+def COPA_eval(premise, choice1, choice2):
+    lprob1 = get_logprobs(premise + "\n" + choice1).sum()
+    lprob2 = get_logprobs(premise + "\n" + choice2).sum()
+    #return 0 if lprob1 > lprob2 else 1
+    return choice1 if lprob1 > lprob2 else choice2
+
+
+
+iface = gr.Interface(
+    fn=COPA_eval,
+    inputs=["text", "text", "text"],
+    outputs=["text"],
+)
+iface.launch()