app.py

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()