训练XLMRobertaForSequenceClassification时：

xlm_r_model(input_ids = X_train_batch_input_ids
            , attention_mask = X_train_batch_attention_mask
            , return_dict = False
           )

我遇到了以下错误：

Traceback (most recent call last):
  File "<string>", line 3, in <module>
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/transformers/models/roberta/modeling_roberta.py", line 1218, in forward
    return_dict=return_dict,
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/transformers/models/roberta/modeling_roberta.py", line 849, in forward
    past_key_values_length=past_key_values_length,
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/transformers/models/roberta/modeling_roberta.py", line 132, in forward
    inputs_embeds = self.word_embeddings(input_ids)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/sparse.py", line 160, in forward
    self.norm_type, self.scale_grad_by_freq, self.sparse)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py", line 2044, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
IndexError: index out of range in self

详情如下：
1.正在创建模型

config = XLMRobertaConfig() 
config.output_hidden_states = False
xlm_r_model = XLMRobertaForSequenceClassification(config=config)
xlm_r_model.to(device) # device is device(type='cpu')

1.标记器

xlmr_tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')

MAX_TWEET_LEN = 402

>>> df_1000.info() # describing a data frame I have pre populated
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1000 entries, 29639 to 44633
Data columns (total 2 columns):
#    Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
0    text    1000 non-null   object
1    class   1000 non-null   int64 
dtypes: int64(1), object(1)
memory usage: 55.7+ KB

X_train = xlmr_tokenizer(list(df_1000[:800].text), padding=True, max_length=MAX_TWEET_LEN+5, truncation=True) # +5: a head room for special tokens / separators
>>> list(map(len,X_train['input_ids']))  # why its 105? shouldn't it be MAX_TWEET_LEN+5 = 407?
[105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, ...]

>>> type(train_index) # describing (for clarity) training fold indices I pre populated
<class 'numpy.ndarray'>

>>> train_index.size 
640

X_train_fold_input_ids = np.array(X_train['input_ids'])[train_index]
X_train_fold_attention_mask = np.array(X_train['attention_mask'])[train_index]

>>> i # batch id
0
>>> batch_size
16

X_train_batch_input_ids = X_train_fold_input_ids[i:i+batch_size]
X_train_batch_input_ids = torch.tensor(X_train_batch_input_ids,dtype=torch.long).to(device)

X_train_batch_attention_mask = X_train_fold_attention_mask[i:i+batch_size]
X_train_batch_attention_mask = torch.tensor(X_train_batch_attention_mask,dtype=torch.long).to(device)

>>> X_train_batch_input_ids.size()
torch.Size([16, 105]) # why 105? Shouldnt this be MAX_TWEET_LEN+5 = 407?

>>> X_train_batch_attention_mask.size()
torch.Size([16, 105]) # why 105? Shouldnt this be MAX_TWEET_LEN+5 = 407?

在此之后，我按照本问题开头的说明调用xlm_r_model(...)，并以指定的错误结束。
注意到所有这些细节，我仍然不能得到为什么我会得到指定的错误。我在哪里做错了？