Add introduction about unimc

app.py

@@ -446,7 +446,7 @@ class UniMCPredict:
         batch = [self.data_model.train_data.encode(
             sample) for sample in batch_data]
         batch = self.data_model.collate_fn(batch)
-        # batch = {k: v.cuda() for k, v in batch.items()}
+        batch = {k: v.to(self.model.model.device) for k, v in batch.items()}
         _, _, logits = self.model.model(**batch)
         soft_logits = torch.nn.functional.softmax(logits, dim=-1)
         logits = torch.argmax(soft_logits, dim=-1).detach().cpu().numpy()
@@ -695,7 +695,13 @@ def main():
         model = load_model('IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese')
     else:
         model = load_model('IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English')
-    
+        
+    st.markdown("""
+            UniMC 核心思想是将自然语言理解任务转化为 multiple choice 任务，其通过控制位置编码和attention mask来让模型可以直接复用 MaskLM head 的参数。这使得 UniMC 仅仅使用 multiple choice 数据集训练就可以超越千亿参数模型在zero-shot场景下。在中文数据集中，UniMC 同样超越了其他模型，获得了FewCLUE和ZeroCLUE两个榜单的第一。
+            
+            The core idea of UniMC is to convert the natural language understanding task into a multiple choice task, which allows the model to directly reuse the parameters of the MaskLM head by controlling the position encoding and attention mask. This enables UniMC to surpass 100 billion parameter models in zero-shot scenarios just by training with multiple choice datasets. In the Chinese dataset, UniMC also surpassed other models and won the first place in both FewCLUE and ZeroCLUE.
+            """)
+            
     st.info("Please input the following information「请输入以下信息...」")
     model_type = st.selectbox('Select task type「选择任务类型」',['Text classification「文本分类」','Sentiment「情感分析」','Similarity「语义匹配」','NLI 「自然语言推理」','Multiple Choice「多项式阅读理解」'])
     form = st.form("参数设置")
@@ -706,7 +712,7 @@ def main():
     else:
         sentences = form.text_area("Please input the context「请输入句子」", text_dict_en[model_type])
         question = form.text_input("Please input the question「请输入问题（不输入问题也可以）」", question_dict_en[model_type])
-        choice = form.text_input("Please input the label(split by ‘;’)「输入标签（以中文；分割）」", choice_dict_en[model_type])
+        choice = form.text_input("Please input the label(split by ‘;’)「输入标签（以英文;分割）」", choice_dict_en[model_type])
     
     form.form_submit_button("Submit「点击一下，开始预测！」")
     
@@ -724,7 +730,8 @@ def main():
 
 
     start=time.time()
-    result = model.predict(data, cuda=False)
+    is_cuda= True if torch.cuda.is_available() else False
+    result = model.predict(data, cuda=is_cuda)
     st.success(f"Prediction is successful, consumes {str(time.time()-start)} seconds")
     st.json(result[0])