---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-42-60cc3e58410d> in <module>
      3 print()
      4 print(df['review'][5171])
----> 5 nlp(new_texts[5170][-1990:])

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/transformers/pipelines/text_classification.py in __call__(self, *args, **kwargs)
    124             If ``self.return_all_scores=True``, one such dictionary is returned per label.
    125         """
--> 126         return super().__call__(*args, **kwargs)
    127 
    128     def preprocess(self, inputs, **tokenizer_kwargs) -> Dict[str, GenericTensor]:

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/transformers/pipelines/base.py in __call__(self, inputs, num_workers, *args, **kwargs)
    922             return self.get_iterator(inputs, num_workers, preprocess_params, forward_params, postprocess_params)
    923         else:
--> 924             return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)
    925 
    926     def run_multi(self, inputs, preprocess_params, forward_params, postprocess_params):

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/transformers/pipelines/text_classification.py in run_single(self, inputs, preprocess_params, forward_params, postprocess_params)
    170     def run_single(self, inputs, preprocess_params, forward_params, postprocess_params):
    171         "This pipeline is odd, and return a list when single item is run"
--> 172         return [super().run_single(inputs, preprocess_params, forward_params, postprocess_params)]

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/transformers/pipelines/base.py in run_single(self, inputs, preprocess_params, forward_params, postprocess_params)
    929     def run_single(self, inputs, preprocess_params, forward_params, postprocess_params):
    930         model_inputs = self.preprocess(inputs, **preprocess_params)
--> 931         model_outputs = self.forward(model_inputs, **forward_params)
    932         outputs = self.postprocess(model_outputs, **postprocess_params)
    933         return outputs

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/transformers/pipelines/base.py in forward(self, model_inputs, **forward_params)
    873             if self.framework == "tf":
    874                 model_inputs["training"] = False
--> 875                 model_outputs = self._forward(model_inputs, **forward_params)
    876             elif self.framework == "pt":
    877                 inference_context = self.get_inference_context()

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/transformers/pipelines/text_classification.py in _forward(self, model_inputs)
    131 
    132     def _forward(self, model_inputs):
--> 133         return self.model(**model_inputs)
    134 
    135     def postprocess(self, model_outputs, function_to_apply=None, return_all_scores=False):

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
   1035         with autocast_variable.enable_auto_cast_variables(
   1036             self._compute_dtype_object):
-> 1037           outputs = call_fn(inputs, *args, **kwargs)
   1038 
   1039         if self._activity_regularizer:

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/transformers/models/roberta/modeling_tf_roberta.py in call(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict, labels, training, **kwargs)
   1435             kwargs_call=kwargs,
   1436         )
-> 1437         outputs = self.roberta(
   1438             inputs["input_ids"],
   1439             attention_mask=inputs["attention_mask"],

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
   1035         with autocast_variable.enable_auto_cast_variables(
   1036             self._compute_dtype_object):
-> 1037           outputs = call_fn(inputs, *args, **kwargs)
   1038 
   1039         if self._activity_regularizer:

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/transformers/models/roberta/modeling_tf_roberta.py in call(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, training, **kwargs)
    674             inputs["token_type_ids"] = tf.fill(dims=input_shape, value=0)
    675 
--> 676         embedding_output = self.embeddings(
    677             input_ids=inputs["input_ids"],
    678             position_ids=inputs["position_ids"],

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
   1035         with autocast_variable.enable_auto_cast_variables(
   1036             self._compute_dtype_object):
-> 1037           outputs = call_fn(inputs, *args, **kwargs)
   1038 
   1039         if self._activity_regularizer:

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/transformers/models/roberta/modeling_tf_roberta.py in call(self, input_ids, position_ids, token_type_ids, inputs_embeds, past_key_values_length, training)
    167                 position_ids = tf.tile(input=position_ids, multiples=(input_shape[0], 1))
    168 
--> 169         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
    170         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
    171         final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
    204     """Call target, and fall back on dispatchers if there is a TypeError."""
    205     try:
--> 206       return target(*args, **kwargs)
    207     except (TypeError, ValueError):
    208       # Note: convert_to_eager_tensor currently raises a ValueError, not a

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py in gather_v2(params, indices, validate_indices, axis, batch_dims, name)
   5067               batch_dims=0,
   5068               name=None):
-> 5069   return gather(
   5070       params,
   5071       indices,

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs)
    547                 'in a future version' if date is None else ('after %s' % date),
    548                 instructions)
--> 549       return func(*args, **kwargs)
    550 
    551     doc = _add_deprecated_arg_notice_to_docstring(

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
    204     """Call target, and fall back on dispatchers if there is a TypeError."""
    205     try:
--> 206       return target(*args, **kwargs)
    207     except (TypeError, ValueError):
    208       # Note: convert_to_eager_tensor currently raises a ValueError, not a

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py in gather(***failed resolving arguments***)
   5054     # TODO(apassos) find a less bad way of detecting resource variables
   5055     # without introducing a circular dependency.
-> 5056     return params.sparse_read(indices, name=name)
   5057   except AttributeError:
   5058     return gen_array_ops.gather_v2(params, indices, axis, name=name)

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/tensorflow/python/ops/resource_variable_ops.py in sparse_read(self, indices, name)
    711     with ops.name_scope("Gather" if name is None else name) as name:
    712       variable_accessed(self)
--> 713       value = gen_resource_variable_ops.resource_gather(
    714           self.handle, indices, dtype=self._dtype, name=name)
    715 

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/tensorflow/python/ops/gen_resource_variable_ops.py in resource_gather(resource, indices, dtype, batch_dims, validate_indices, name)
    547       return _result
    548     except _core._NotOkStatusException as e:
--> 549       _ops.raise_from_not_ok_status(e, name)
    550     except _core._FallbackException:
    551       pass

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/tensorflow/python/framework/ops.py in raise_from_not_ok_status(e, name)
   6939   message = e.message + (" name: " + name if name is not None else "")
   6940   # pylint: disable=protected-access
-> 6941   six.raise_from(core._status_to_exception(e.code, message), None)
   6942   # pylint: enable=protected-access
   6943 

/anaconda/envs/azureml_py38_tensorflow/lib/python3.8/site-packages/six.py in raise_from(value, from_value)

InvalidArgumentError: indices[0,512] = 514 is not in [0, 514) [Op:ResourceGather]