zero_shot_explainer not working xlm-roberta-large-xnli-anli

model_name="vicgalle/xlm-roberta-large-xnli-anli"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

IndexError Traceback (most recent call last)
in
3 zero_shot_explainer = ZeroShotClassificationExplainer(model, tokenizer)
4
----> 5 word_attributions = zero_shot_explainer(
6 "国家中国美国",
7 labels = ["国家", "中国", "美国"],

/opt/conda/lib/python3.8/site-packages/transformers_interpret/explainers/zero_shot_classification.py in __call__(self, text, labels, embedding_type, hypothesis_template, include_hypothesis, internal_batch_size, n_steps)
314 self.hypothesis_labels = [hypothesis_template.format(label) for label in labels]
315
--> 316 predicted_text_idx = self._get_top_predicted_label_idx(
317 text, self.hypothesis_labels
318 )

/opt/conda/lib/python3.8/site-packages/transformers_interpret/explainers/zero_shot_classification.py in _get_top_predicted_label_idx(self, text, hypothesis_labels)
143 )
144 attention_mask = self._make_attention_mask(input_ids)
--> 145 preds = self._get_preds(
146 input_ids, token_type_ids, position_ids, attention_mask
147 )

/opt/conda/lib/python3.8/site-packages/transformers_interpret/explainers/question_answering.py in _get_preds(self, input_ids, token_type_ids, position_ids, attention_mask)
212 ):
213 if self.accepts_position_ids and self.accepts_token_type_ids:
--> 214 preds = self.model(
215 input_ids,
216 token_type_ids=token_type_ids,

/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),

/opt/conda/lib/python3.8/site-packages/transformers/models/roberta/modeling_roberta.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)
993 return_dict = return_dict if return_dict is not None else self.config.use_return_dict
994
--> 995 outputs = self.roberta(
996 input_ids,
997 attention_mask=attention_mask,

/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),

/opt/conda/lib/python3.8/site-packages/transformers/models/roberta/modeling_roberta.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, output_attentions, output_hidden_states, return_dict)
685 head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
686
--> 687 embedding_output = self.embeddings(
688 input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
689 )

/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),

/opt/conda/lib/python3.8/site-packages/transformers/models/roberta/modeling_roberta.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds)
117 inputs_embeds = self.word_embeddings(input_ids)
118 position_embeddings = self.position_embeddings(position_ids)
--> 119 token_type_embeddings = self.token_type_embeddings(token_type_ids)
120
121 embeddings = inputs_embeds + position_embeddings + token_type_embeddings

/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),

/opt/conda/lib/python3.8/site-packages/torch/nn/modules/sparse.py in forward(self, input)
122
123 def forward(self, input: Tensor) -> Tensor:
--> 124 return F.embedding(
125 input, self.weight, self.padding_idx, self.max_norm,
126 self.norm_type, self.scale_grad_by_freq, self.sparse)

/opt/conda/lib/python3.8/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
1812 # remove once script supports set_grad_enabled
1813 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 1814 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
1815
1816

IndexError: index out of range in self