getting error on very first demo code

import aspect_based_sentiment_analysis as absa

nlp = absa.load()
text = ("We are great fans of Slack, but we wish the subscriptions "
"were more accessible to small startups.")

slack, price = nlp(text, aspects=['slack', 'price'])
assert price.sentiment == absa.Sentiment.negative
assert slack.sentiment == absa.Sentiment.positive

RecursionError Traceback (most recent call last) Cell In[7], line 1 ----> 1 nlp = absa.load()

File /opt/conda/lib/python3.10/site-packages/aspect_based_sentiment_analysis/loads.py:34, in load(name, text_splitter, reference_recognizer, pattern_recognizer, model_kwargs) 32 try: 33 config = BertABSCConfig.from_pretrained(name, model_kwargs) ---> 34 model = BertABSClassifier.from_pretrained(name, config=config) 35 tokenizer = transformers.BertTokenizer.from_pretrained(name) 36 professor = Professor(reference_recognizer, pattern_recognizer)

File /opt/conda/lib/python3.10/site-packages/transformers/modeling_tf_utils.py:2949, in TFPreTrainedModel.from_pretrained(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, *kwargs) 2946 model_kwargs["load_weight_prefix"] = load_weight_prefix + "/" + model_kwargs.get("name") 2948 # Instantiate model. -> 2949 model = cls(config, model_args, **model_kwargs) 2951 if tf_to_pt_weight_rename is None and hasattr(model, "tf_to_pt_weight_rename"): 2952 # TODO Matt: This is a temporary workaround to allow weight renaming, but requires a method 2953 # to be defined for each class that requires a rename. We can probably just have a class-level 2954 # dict and a single top-level method or something and cut down a lot of boilerplate code 2955 tf_to_pt_weight_rename = model.tf_to_pt_weight_rename

File /opt/conda/lib/python3.10/site-packages/keras/src/models/model.py:146, in Model.new(cls, *args, *kwargs) 143 from keras.src.models import functional 145 return functional.Functional(args, **kwargs) --> 146 return typing.cast(Model, super().new(cls))

File /opt/conda/lib/python3.10/site-packages/keras/src/layers/layer.py:231, in Layer.new(cls, *args, **kwargs) 228 obj._post_build() 229 obj._lock_state() --> 231 obj.build = build_wrapper 232 return obj

File /opt/conda/lib/python3.10/site-packages/keras/src/layers/layer.py:1352, in Layer.setattr(self, name, value) 1350 value = self._tracker.track(value) 1351 elif name != "_tracker": -> 1352 self._initialize_tracker() 1353 return super().setattr(name, value)

File /opt/conda/lib/python3.10/site-packages/keras/src/utils/tracking.py:26, in no_automatic_dependency_tracking..wrapper(*args, kwargs) 23 @wraps(fn) 24 def wrapper(*args, *kwargs): 25 with DotNotTrackScope(): ---> 26 return fn(args, kwargs)

File /opt/conda/lib/python3.10/site-packages/keras/src/layers/layer.py:307, in Layer._initialize_tracker(self) 305 metrics = [] 306 seed_generators = [] --> 307 self._tracker = tracking.Tracker( 308 { 309 "trainable_variables": ( 310 lambda x: isinstance(x, backend.Variable) and x.trainable, 311 trainable_variables, 312 ), 313 "non_trainable_variables": ( 314 lambda x: isinstance(x, backend.Variable) 315 and not x.trainable, 316 non_trainable_variables, 317 ), 318 "metrics": (lambda x: isinstance(x, Metric), metrics), 319 "layers": ( 320 lambda x: isinstance(x, Layer) 321 and not isinstance(x, Metric), 322 layers, 323 ), 324 "seed_generators": ( 325 lambda x: isinstance(x, backend.random.SeedGenerator), 326 seed_generators, 327 ), 328 }, 329 exclusions={"non_trainable_variables": ["trainable_variables"]}, 330 ) 331 if backend.backend() == "tensorflow": 332 # Remove attribute tracking for lists (TF-specific attribute) 333 _self_setattr_tracking = getattr( 334 self, "_self_setattr_tracking", True 335 )

File /opt/conda/lib/python3.10/site-packages/keras/src/layers/layer.py:1353, in Layer.setattr(self, name, value) 1351 elif name != "_tracker": 1352 self._initialize_tracker() -> 1353 return super().setattr(name, value)

File /opt/conda/lib/python3.10/site-packages/keras/src/backend/tensorflow/trackable.py:28, in KerasAutoTrackable.setattr(self, name, value) 24 if getattr(self, "_self_setattr_tracking", True): 25 value = sticky_attribute_assignment( 26 trackable=self, value=value, name=name 27 ) ---> 28 super().setattr(name, value)

File /opt/conda/lib/python3.10/site-packages/tf_keras/src/engine/training.py:388, in Model.setattr(self, name, value) 381 except AttributeError: 382 raise RuntimeError( 383 "It looks like you are subclassing Model and you " 384 "forgot to call super().init()." 385 " Always start with this line." 386 ) --> 388 super().setattr(name, value)

File /opt/conda/lib/python3.10/site-packages/tf_keras/src/engine/base_layer.py:3152, in Layer.setattr(self, name, value) 3147 # Wraps data structures in Trackable, unwraps NoDependency objects. 3148 value = tf.internal.tracking.sticky_attribute_assignment( 3149 trackable=self, value=value, name=name 3150 ) -> 3152 reference_counts = self._obj_reference_counts 3153 reference_counts[value] = reference_counts.get(value, 0) + 1 3155 # When replacing an existing tf.Variable with a new one, we want to 3156 # check its existing position in the 3157 # self._trainable/non_trainable_variable, so that we can put it back to 3158 # the original position.

File /opt/conda/lib/python3.10/site-packages/tf_keras/src/engine/base_layer.py:3042, in Layer._obj_reference_counts(self) 3039 @property 3040 def _obj_reference_counts(self): 3041 """A dict counting the number of attributes referencing an object.""" -> 3042 self._maybe_create_attribute( 3043 "_obj_reference_counts_dict", 3044 object_identity.ObjectIdentityDictionary(), 3045 ) 3046 return self._obj_reference_counts_dict

File /opt/conda/lib/python3.10/site-packages/tensorflow/python/trackable/base.py:202, in no_automatic_dependency_tracking.._method_wrapper(self, *args, kwargs) 200 def _method_wrapper(self, *args, *kwargs): 201 previous_value = getattr(self, "_self_setattr_tracking", True) --> 202 self._self_setattr_tracking = False # pylint: disable=protected-access 203 try: 204 result = method(self, args, kwargs)

[... skipping similar frames: Layer.setattr at line 1352 (327 times), Layer.setattr at line 1353 (327 times), Layer._initialize_tracker at line 307 (327 times), no_automatic_dependency_tracking..wrapper at line 26 (327 times), KerasAutoTrackable.setattr at line 28 (326 times), Model.setattr at line 388 (326 times), Layer.setattr at line 3152 (326 times), no_automatic_dependency_tracking.._method_wrapper at line 202 (326 times), Layer._obj_reference_counts at line 3042 (326 times)] File /opt/conda/lib/python3.10/site-packages/keras/src/backend/tensorflow/trackable.py:28, in KerasAutoTrackable.setattr(self, name, value) 24 if getattr(self, "_self_setattr_tracking", True): 25 value = sticky_attribute_assignment( 26 trackable=self, value=value, name=name 27 ) ---> 28 super().setattr(name, value)

File /opt/conda/lib/python3.10/site-packages/keras/src/backend/tensorflow/trackable.py:25, in KerasAutoTrackable.setattr(self, name, value) 22 pass 24 if getattr(self, "_self_setattr_tracking", True): ---> 25 value = sticky_attribute_assignment( 26 trackable=self, value=value, name=name 27 ) 28 super().setattr(name, value)

File /opt/conda/lib/python3.10/site-packages/keras/src/backend/tensorflow/trackable.py:43, in sticky_attribute_assignment(trackable, name, value) 31 def sticky_attribute_assignment(trackable, name, value): 32 """Adds dependencies, called from setattr. 33 34 Args: (...) 41 The value which should be stored in the attribute. 42 """ ---> 43 if isinstance( 44 value, (tracking.TrackedList, tracking.TrackedDict, tracking.TrackedSet) 45 ) and hasattr(trackable, "_tracked"): 46 trackable._tracked.append(name) 47 if not tracking.is_tracking_enabled():

RecursionError: maximum recursion depth exceeded in instancecheck

ScalaConsultants / Aspect-Based-Sentiment-Analysis

getting error on very first demo code #87