title: Length of values (98) does not match length of index (2456)
body: <p>I am working on a <a href="https://en.wikipedia.org/wiki/Project_Jupyter#Google_Colaboratory" rel="nofollow noreferrer">Colaboratory</a> project for sentiment analysis of <a href="https://en.wikipedia.org/wiki/COVID-19" rel="nofollow noreferrer">COVID-19</a> vaccines via a Twitter data set. I am getting the error as below:</p>
  <pre class="lang-none prettyprint-override"><code>ValueError   Traceback (most recent call last)
  &lt;ipython-input-53-a0fa01c42fd3&gt; in &lt;module&gt;()
        3 df_vax = df_vax.fillna(0)
        4 df
  ----&gt; 5 df_vax['Sentiment'] = predictions
  
  3 frames
  /usr/local/lib/python3.7/dist-packages/pandas/core/common.py in require_length_match(data, index)
      530     if len(data) != len(index):
      531         raise ValueError(
  --&gt; 532             &quot;Length of values &quot;
      533             f&quot;({len(data)}) &quot;
      534             &quot;does not match length of index &quot;
  
  ValueError: Length of values (98) does not match length of index (2456)
  [enter image description here][1]
  </code></pre>
  <p>How can I fix this?</p>
  <pre><code>print('Predicting labels for {:,} vaccine tweets...'.format(len(input_ids)))
  
  model.eval()
  predictions , true_labels = [], []
  
  for batch in vax_dataloader:
    batch = tuple(t.to(device) for t in batch)
    b_input_ids, b_input_mask = batch
  
    with torch.no_grad():
        result = model(b_input_ids,
              token_type_ids=None,
              attention_mask=b_input_mask)
  
    logits = result[0]
    logits = logits.detach().cpu().numpy()
    label_ids = b_labels.to('cpu').numpy()
  
    predictions.append(logits)
    true_labels.append(label_ids)
  
  print('Prediction completed')
  
  prediction_set = []
  
  for i in range(len(true_labels)):
    pred_labels_i = np.argmax(predictions[i], axis=1).flatten()
    prediction_set.append(pred_labels_i)
  
  prediction_scores = [item for sublist in prediction_set for item in sublist]
  
  df_vax = df_vax.fillna(0)
  
  df_vax['Sentiment'] = predictions
  </code></pre>