Trying to train model for Intent Recognition but getting float error

I'm trying to train the model for intent recognition. I tried removing all special characters and stop words but unable to resolve this error. I tried removing integers also but it's throwing an error. My data has two columns with one text and one intent column

The code I've written is

class IntentDetectionData:
  DATA_COLUMN = "text"
  LABEL_COLUMN = "intent"

  def __init__(self, train, test, tokenizer: FullTokenizer, classes, max_seq_len=192):
    self.tokenizer = tokenizer
    self.max_seq_len = 0
    self.classes = classes
    
    train, test = map(lambda df: df.reindex(df[IntentDetectionData.DATA_COLUMN].str.len().sort_values().index), [train, test])
    
    ((self.train_x, self.train_y), (self.test_x, self.test_y)) = map(self._prepare, [train, test])

    print("max seq_len", self.max_seq_len)
    self.max_seq_len = min(self.max_seq_len, max_seq_len)
    self.train_x, self.test_x = map(self._pad, [self.train_x, self.test_x])

  def _prepare(self, df):
    x, y = [], []
    
    for _, row in tqdm(df.iterrows()):
      text, label = row[IntentDetectionData.DATA_COLUMN], row[IntentDetectionData.LABEL_COLUMN]
      tokens = self.tokenizer.tokenize(text)
      tokens = ["[CLS]"] + tokens + ["[SEP]"]
      token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
      self.max_seq_len = max(self.max_seq_len, len(token_ids))
      x.append(token_ids)
      y.append(self.classes.index(label))

    return np.array(x), np.array(y)

  def _pad(self, ids):
    x = []
    for input_ids in ids:
      input_ids = input_ids[:min(len(input_ids), self.max_seq_len - 2)]
      input_ids = input_ids + [0] * (self.max_seq_len - len(input_ids))
      x.append(np.array(input_ids))
    return np.array(x)

The next function is

def create_model(max_seq_len, bert_ckpt_file):

  with tf.io.gfile.GFile(bert_config_file, "r") as reader:
      bc = StockBertConfig.from_json_string(reader.read())
      bert_params = map_stock_config_to_params(bc)
      bert_params.adapter_size = None
      bert = BertModelLayer.from_params(bert_params, name="bert")
        
  input_ids = keras.layers.Input(shape=(max_seq_len, ), dtype='int32', name="input_ids")
  bert_output = bert(input_ids)

  print("bert shape", bert_output.shape)

  cls_out = keras.layers.Lambda(lambda seq: seq[:, 0, :])(bert_output)
  cls_out = keras.layers.Dropout(0.5)(cls_out)
  logits = keras.layers.Dense(units=768, activation="tanh")(cls_out)
  logits = keras.layers.Dropout(0.5)(logits)
  logits = keras.layers.Dense(units=len(classes), activation="softmax")(logits)

  model = keras.Model(inputs=input_ids, outputs=logits)
  model.build(input_shape=(None, max_seq_len))

  load_stock_weights(bert, bert_ckpt_file)
        
  return model

The next code is:

classes = train.intent.unique().tolist()

data = IntentDetectionData(train, test, tokenizer, classes, max_seq_len=10000)

After running the above code I'm getting error like

ValueError: Unsupported string type: <class 'float'>