BERT encoding for batch of Sentence Pairs raise IndexError: index out of range in self
I’m trying to get BERT encoding for sentence pairs.I manage to get the tokens for the batch of sentences. But when I feed it to BERT I get :IndexError: index out of range in selfFor a single pair of sentences, the BERT model does accept the tokens. But for lists of sentences, I get this error. I don’t understand where is the problem.( The model I am using inherits from BERT ).Here is my code:alephbert_tokenizer = AutoTokenizer.from_pretrained('onlplab/alephbert-base')  
alephbert = AutoModel.from_pretrained('onlplab/alephbert-base')

sentence_a = ['שני דגים שחו בים','שניהם צללו לים','כמה נשארו?','שלושה דגים שחו בנהר', 'אחד צלל לים'] 
sentence_b = ['אחת שתיים שלוש','בקול גדול גדול','רק ישבו ופטפטו אחת שתיים שלוש','מאחורי ההר ישבו שלושה גמדים', 'לא אכלו ולא שתו אחת שתיים שלוש'] 

tokens = alephbert_tokenizer(sentence_a, sentence_b, 
                                padding = 'max_length', 
                                truncation = True,
                                max_length = 512, 
                                return_tensors = 'pt')

batch_input_ids = tokens['input_ids']
batch_token_type_ids = tokens['token_type_ids']
batch_attention_mask = tokens['attention_mask']

alephbert(batch['input_ids'], batch['attention_mask'], batch['token_type_ids'])Trackback:IndexError                                Traceback (most recent call last)
Input In [76], in <cell line: 1>()
----> 1 alephbert(batch['input_ids'], batch['attention_mask'], batch['token_type_ids'])

File C:\Users\BUDBUDIO\Anaconda3\lib\site-packages\torch\nn\modules\module.py:889, in Module._call_impl(self, *input, **kwargs)
    887     result = self._slow_forward(*input, **kwargs)
    888 else:
--> 889     result = self.forward(*input, **kwargs)
    890 for hook in itertools.chain(
    891         _global_forward_hooks.values(),
    892         self._forward_hooks.values()):
    893     hook_result = hook(self, input, result)

File C:\Users\BUDBUDIO\Anaconda3\lib\site-packages\transformers\models\bert\modeling_bert.py:1010, in BertModel.forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)
   1003 # Prepare head mask if needed
   1004 # 1.0 in head_mask indicate we keep the head
   1005 # attention_probs has shape bsz x n_heads x N x N
   1006 # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
   1007 # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
   1008 head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-> 1010 embedding_output = self.embeddings(
   1011     input_ids=input_ids,
   1012     position_ids=position_ids,
   1013     token_type_ids=token_type_ids,
   1014     inputs_embeds=inputs_embeds,
   1015     past_key_values_length=past_key_values_length,
   1016 )
   1017 encoder_outputs = self.encoder(
   1018     embedding_output,
   1019     attention_mask=extended_attention_mask,
   (...)
   1027     return_dict=return_dict,
   1028 )
   1029 sequence_output = encoder_outputs[0]

File C:\Users\BUDBUDIO\Anaconda3\lib\site-packages\torch\nn\modules\module.py:889, in Module._call_impl(self, *input, **kwargs)
    887     result = self._slow_forward(*input, **kwargs)
    888 else:
--> 889     result = self.forward(*input, **kwargs)
    890 for hook in itertools.chain(
    891         _global_forward_hooks.values(),
    892         self._forward_hooks.values()):
    893     hook_result = hook(self, input, result)

File C:\Users\BUDBUDIO\Anaconda3\lib\site-packages\transformers\models\bert\modeling_bert.py:236, in BertEmbeddings.forward(self, input_ids, token_type_ids, position_ids, inputs_embeds, past_key_values_length)
    234 if inputs_embeds is None:
    235     inputs_embeds = self.word_embeddings(input_ids)
--> 236 token_type_embeddings = self.token_type_embeddings(token_type_ids)
    238 embeddings = inputs_embeds + token_type_embeddings
    239 if self.position_embedding_type == "absolute":

File C:\Users\BUDBUDIO\Anaconda3\lib\site-packages\torch\nn\modules\module.py:889, in Module._call_impl(self, *input, **kwargs)
    887     result = self._slow_forward(*input, **kwargs)
    888 else:
--> 889     result = self.forward(*input, **kwargs)
    890 for hook in itertools.chain(
    891         _global_forward_hooks.values(),
    892         self._forward_hooks.values()):
    893     hook_result = hook(self, input, result)

File C:\Users\BUDBUDIO\Anaconda3\lib\site-packages\torch\nn\modules\sparse.py:156, in Embedding.forward(self, input)
    155 def forward(self, input: Tensor) -> Tensor:
--> 156     return F.embedding(
    157         input, self.weight, self.padding_idx, self.max_norm,
    158         self.norm_type, self.scale_grad_by_freq, self.sparse)

File C:\Users\BUDBUDIO\Anaconda3\lib\site-packages\torch\nn\functional.py:1916, in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
   1910     # Note [embedding_renorm set_grad_enabled]
   1911     # XXX: equivalent to
   1912     # with torch.no_grad():
   1913     #   torch.embedding_renorm_
   1914     # remove once script supports set_grad_enabled
   1915     _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 1916 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)

IndexError: index out of range in selfI also paste here the working example for one pair for reference:sentence_a1 = 'אני אוהב שוקולד, ועוגות גבינה'
sentence_b1 = 'וארטיק וסוכריות ותות גינה'

single_s_tok = alephbert_tokenizer(sentence_a1,sentence_b1, is_split_into_words=True)  
input_ids = torch.tensor(single_s_tok['input_ids']).unsqueeze(0)
attention_mask = torch.tensor(single_s_tok['attention_mask']).unsqueeze(0)
token_type_ids = torch.tensor(single_s_tok['token_type_ids']).unsqueeze(0)
alephbert(input_ids, attention_mask, token_type_ids)Any help is greatly appreciated.
2022-11-16T20:40:38Z
Please Help, I’m lost here