随便啦

本文介绍了一种使用Java实现的数组搜索方法,包括线性搜索和二分搜索,并提供了两种基本的排序算法实现:冒泡排序和选择排序。

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 public class TestSearch {
 public static void main(String[] args) {
  int a[] = { 1, 3, 6, 8, 9, 10, 12, 18, 20, 34 };
  int i = 12;
  //System.out.println(search(a, i));
  System.out.println(binarySearch(a, i));
 }
 
 public static int search(int[] a, int num) {
  for(int i=0; i<a.length; i++) {
   if(a[i] == num) return i;
  }
  return -1;
 }
 
 public static int binarySearch(int[]a, int num) {
    if (a.length==0) return -1;
   
    int startPos = 0;
    int endPos = a.length-1;
    int m = (startPos + endPos) / 2;
    while(startPos <= endPos){
      if(num == a[m]) return m;
      if(num > a[m]) {
       startPos = m + 1;
      }
      if(num < a[m]) {
       endPos = m -1;
      }
      m = (startPos + endPos) / 2;
    }
    return -1;
  }

}

以上代码有以下问题,分析修改:(style_tune) C:\Users\28996\Desktop\AI\persona_contrastive_finetuning>python Contrastive_Training_LM.py Map: 0%| | 0/1 [00:00<?, ? examples/s]ERROR:__main__:无法解析anchor_input_ids: 你如何看待气候变化? ERROR:__main__:无法解析positive_input_ids: 气候变化是严峻的全球危机,我们需要立即采取行动减少碳排放! ERROR:__main__:无法解析negative_input_ids: 哈哈天气什么的随便啦,不如聊聊游戏? Map: 100%|████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 13.02 examples/s] Map: 0%| | 0/1 [00:00<?, ? examples/s]ERROR:__main__:无法解析anchor_input_ids: 你如何看待气候变化? ERROR:__main__:无法解析positive_input_ids: 气候变化是严峻的全球危机,我们需要立即采取行动减少碳排放! ERROR:__main__:无法解析negative_input_ids: 哈哈天气什么的随便啦,不如聊聊游戏? Map: 100%|████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 67.37 examples/s] 训练集样本示例: {'anchor_input_ids': '你如何看待气候变化?', 'positive_input_ids': '气候变化是严峻的全球危机,我们需要立即采取行动减少碳排放!', 'negative_input_ids': '哈哈天气什么的随便啦,不如聊聊游戏?'} 验证集样本示例: {'anchor_input_ids': '你如何看待气候变化?', 'positive_input_ids': '气候变化是严峻的全球危机,我们需要立即采取行动减少碳排放!', 'negative_input_ids': '哈哈天气什么的随便啦,不如聊聊游戏?'} 0%| | 0/3 [00:00<?, ?it/s]ERROR:__main__:无法解析token IDs: 你如何看待气候变化? ERROR:__main__:无法解析token IDs: 气候变化是严峻的全球危机,我们需要立即采取行动减少碳排放! ERROR:__main__:无法解析token IDs: 哈哈天气什么的随便啦,不如聊聊游戏? You're using a Qwen2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. Traceback (most recent call last): File "C:\Users\28996\Desktop\AI\persona_contrastive_finetuning\Contrastive_Training_LM.py", line 281, in <module> trainer.train() File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\trainer.py", line 2171, in train return inner_training_loop( File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\trainer.py", line 2480, in _inner_training_loop batch_samples, num_items_in_batch = self.get_batch_samples(epoch_iterator, num_batches) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\trainer.py", line 5156, in get_batch_samples batch_samples += [next(epoch_iterator)] File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\accelerate\data_loader.py", line 567, in __iter__ current_batch = next(dataloader_iter) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\utils\data\dataloader.py", line 701, in __next__ data = self._next_data() File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\utils\data\dataloader.py", line 757, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\utils\data\_utils\fetch.py", line 55, in fetch return self.collate_fn(data) File "C:\Users\28996\Desktop\AI\persona_contrastive_finetuning\Contrastive_Training_LM.py", line 96, in __call__ "positive_attention_mask": create_to_attention_mask(batch_positive["input_ids"]), NameError: name 'create_to_attention_mask' is not defined. Did you mean: 'create_attention_mask'? 0%| | 0/3 [00:00<?, ?it/s]
07-21
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