tensorflow.python.framework.errors_impl.InvalidArgumentError: indices[0,529] = 529 is not in [0, 512) [Op:ResourceGather]
chetanniradwar opened this issue · 0 comments
chetanniradwar commented
I search on internet and find that this issue is due the length of the 'text' I am passing to the below nlp function.
import aspect_based_sentiment_analysis as absa
name = 'absa/classifier-lapt-0.2'
recognizer = absa.aux_models.BasicPatternRecognizer()
nlp = absa.load(name,pattern_recognizer=recognizer)
text= " " #some multiline long review text
completed_task = nlp(text, aspects=['camera','design'])
camera,design = completed_task.examples
print(camera.sentiment)
when I reduced the size of the text. It is working fine. Any other solution to this
Traceback (most recent call last):
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-28-07dbd638851c> in <module>
17 aspect='camera'
18 camera=0
---> 19 completed_task = nlp(text, aspects=['camera','design'])
20 camera,design = completed_task.examples
~\AppData\Local\Programs\Python\Python37\lib\site-packages\aspect_based_sentiment_analysis\pipelines.py in __call__(self, text, aspects)
206 def __call__(self, text: str, aspects: List[str]) -> CompletedTask:
207 task = self.preprocess(text, aspects)
--> 208 predictions = self.transform(task.examples)
209 completed_task = self.postprocess(task, predictions)
210 return completed_task
~\AppData\Local\Programs\Python\Python37\lib\site-packages\aspect_based_sentiment_analysis\pipelines.py in transform(self, examples)
222 tokenized_examples = self.tokenize(examples)
223 input_batch = self.encode(tokenized_examples)
--> 224 output_batch = self.predict(input_batch)
225 predictions = self.review(tokenized_examples, output_batch)
226 return predictions
~\AppData\Local\Programs\Python\Python37\lib\site-packages\aspect_based_sentiment_analysis\pipelines.py in predict(self, input_batch)
252 token_ids=input_batch.token_ids,
253 attention_mask=input_batch.attention_mask,
--> 254 token_type_ids=input_batch.token_type_ids
255 )
256 # We assume that our predictions are correct. This is
~\AppData\Local\Programs\Python\Python37\lib\site-packages\aspect_based_sentiment_analysis\models.py in call(self, token_ids, attention_mask, token_type_ids, training, **bert_kwargs)
144 token_type_ids=token_type_ids,
145 training=training,
--> 146 **bert_kwargs
147 )
148 sequence_output, pooled_output, hidden_states, attentions = outputs
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__(self, *args, **kwargs)
966 with base_layer_utils.autocast_context_manager(
967 self._compute_dtype):
--> 968 outputs = self.call(cast_inputs, *args, **kwargs)
969 self._handle_activity_regularization(inputs, outputs)
970 self._set_mask_metadata(inputs, outputs, input_masks)
~\AppData\Local\Programs\Python\Python37\lib\site-packages\transformers\modeling_tf_bert.py in call(self, inputs, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, training)
564 # head_mask = tf.constant([0] * self.num_hidden_layers)
565
--> 566 embedding_output = self.embeddings([input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
567 encoder_outputs = self.encoder([embedding_output, extended_attention_mask, head_mask], training=training)
568
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__(self, *args, **kwargs)
966 with base_layer_utils.autocast_context_manager(
967 self._compute_dtype):
--> 968 outputs = self.call(cast_inputs, *args, **kwargs)
969 self._handle_activity_regularization(inputs, outputs)
970 self._set_mask_metadata(inputs, outputs, input_masks)
~\AppData\Local\Programs\Python\Python37\lib\site-packages\transformers\modeling_tf_bert.py in call(self, inputs, mode, training)
146 """
147 if mode == "embedding":
--> 148 return self._embedding(inputs, training=training)
149 elif mode == "linear":
150 return self._linear(inputs)
~\AppData\Local\Programs\Python\Python37\lib\site-packages\transformers\modeling_tf_bert.py in _embedding(self, inputs, training)
169 if inputs_embeds is None:
170 inputs_embeds = tf.gather(self.word_embeddings, input_ids)
--> 171 position_embeddings = self.position_embeddings(position_ids)
172 token_type_embeddings = self.token_type_embeddings(token_type_ids)
173
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__(self, *args, **kwargs)
966 with base_layer_utils.autocast_context_manager(
967 self._compute_dtype):
--> 968 outputs = self.call(cast_inputs, *args, **kwargs)
969 self._handle_activity_regularization(inputs, outputs)
970 self._set_mask_metadata(inputs, outputs, input_masks)
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\layers\embeddings.py in call(self, inputs)
182 if dtype != 'int32' and dtype != 'int64':
183 inputs = math_ops.cast(inputs, 'int32')
--> 184 out = embedding_ops.embedding_lookup(self.embeddings, inputs)
185 return out
186
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\ops\embedding_ops.py in embedding_lookup(params, ids, partition_strategy, name, validate_indices, max_norm)
324 name=name,
325 max_norm=max_norm,
--> 326 transform_fn=None)
327
328
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\ops\embedding_ops.py in _embedding_lookup_and_transform(params, ids, partition_strategy, name, max_norm, transform_fn)
135 with ops.colocate_with(params[0]):
136 result = _clip(
--> 137 array_ops.gather(params[0], ids, name=name), ids, max_norm)
138 if transform_fn:
139 result = transform_fn(result)
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\util\dispatch.py in wrapper(*args, **kwargs)
178 """Call target, and fall back on dispatchers if there is a TypeError."""
179 try:
--> 180 return target(*args, **kwargs)
181 except (TypeError, ValueError):
182 # Note: convert_to_eager_tensor currently raises a ValueError, not a
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\ops\array_ops.py in gather(***failed resolving arguments***)
4520 # TODO(apassos) find a less bad way of detecting resource variables
4521 # without introducing a circular dependency.
-> 4522 return params.sparse_read(indices, name=name)
4523 except AttributeError:
4524 return gen_array_ops.gather_v2(params, indices, axis, name=name)
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py in sparse_read(self, indices, name)
674 variable_accessed(self)
675 value = gen_resource_variable_ops.resource_gather(
--> 676 self._handle, indices, dtype=self._dtype, name=name)
677
678 if self._dtype == dtypes.variant:
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\ops\gen_resource_variable_ops.py in resource_gather(resource, indices, dtype, batch_dims, validate_indices, name)
554 pass # Add nodes to the TensorFlow graph.
555 except _core._NotOkStatusException as e:
--> 556 _ops.raise_from_not_ok_status(e, name)
557 # Add nodes to the TensorFlow graph.
558 dtype = _execute.make_type(dtype, "dtype")
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\framework\ops.py in raise_from_not_ok_status(e, name)
6651 message = e.message + (" name: " + name if name is not None else "")
6652 # pylint: disable=protected-access
-> 6653 six.raise_from(core._status_to_exception(e.code, message), None)
6654 # pylint: enable=protected-access
6655
~\AppData\Local\Programs\Python\Python37\lib\site-packages\six.py in raise_from(value, from_value)
InvalidArgumentError: indices[0,529] = 529 is not in [0, 512) [Op:ResourceGather]