Yes, @nik202 I have a trained model and while trying to train again it is not getting trained.
<frozen importlib._bootstrap>:219: RuntimeWarning: greenlet.greenlet size changed, may indicate binary incompatibility. Expected 144 from C header, got 152 from PyObject
<frozen importlib._bootstrap>:219: RuntimeWarning: greenlet.greenlet size changed, may indicate binary incompatibility. Expected 144 from C header, got 152 from PyObject
<frozen importlib._bootstrap>:219: RuntimeWarning: greenlet.greenlet size changed, may indicate binary incompatibility. Expected 144 from C header, got 152 from PyObject
<frozen importlib._bootstrap>:219: RuntimeWarning: greenlet.greenlet size changed, may indicate binary incompatibility. Expected 144 from C header, got 152 from PyObject
2021-08-06 18:57:30 INFO rasa.shared.utils.validation - The 'version' key is missing in the training data file C:\Users\AppsTek V1\Code\financial-demo-rasa-2-0\domain.yml. Rasa Open Source will read the file as a version '2.0' file. See https://rasa.com/docs/rasa/training-data-format.
2021-08-06 18:57:32 INFO rasa.model - Data (config) for Core model section changed.
2021-08-06 18:57:32 INFO rasa.model - Data (config) for NLU model section changed.
2021-08-06 18:57:32 INFO rasa.model - Data (nlg) for NLG templates section changed.
Training NLU model...
2021-08-06 18:57:43 INFO rasa.nlu.utils.spacy_utils - Trying to load spacy model with name 'en_core_web_md'
2021-08-06 18:57:58 INFO rasa.nlu.components - Added 'SpacyNLP' to component cache. Key 'SpacyNLP-en_core_web_md'.
2021-08-06 18:57:58 INFO rasa.shared.nlu.training_data.training_data - Training data stats:
2021-08-06 18:57:58 INFO rasa.shared.nlu.training_data.training_data - Number of intent examples: 374 (20 distinct intents)
2021-08-06 18:57:58 INFO rasa.shared.nlu.training_data.training_data - Found intents: 'inform', 'affrim', 'pay_cc', 'trigger_handoff', 'search_transactions', 'ask_transfer_charge', 'check_creditscore', 'goodbye', 'check_earnings', 'affirm', 'handoff', 'help', 'human_handoff', 'out_of_scope', 'check_recipients', 'greet', 'thankyou', 'transfer_money', 'deny', 'check_balance'
2021-08-06 18:57:58 INFO rasa.shared.nlu.training_data.training_data - Number of response examples: 0 (0 distinct responses)
2021-08-06 18:57:58 INFO rasa.shared.nlu.training_data.training_data - Number of entity examples: 183 (5 distinct entities)
2021-08-06 18:57:58 INFO rasa.shared.nlu.training_data.training_data - Found entity types: 'account_type', 'PERSON', 'amount-of-money', 'credit_card', 'vendor_name'
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Intent 'affrim' has only 1 training examples! Minimum is 2, training may fail.
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Intent 'handoff' has only 1 training examples! Minimum is 2, training may fail.
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Intent 'trigger_handoff' has only 1 training examples! Minimum is 2, training may fail.
2021-08-06 18:57:58 INFO rasa.nlu.model - Starting to train component WhitespaceTokenizer
2021-08-06 18:57:58 INFO rasa.nlu.model - Finished training component.
2021-08-06 18:57:58 INFO rasa.nlu.model - Starting to train component RegexFeaturizer
2021-08-06 18:57:58 INFO rasa.nlu.model - Finished training component.
2021-08-06 18:57:58 INFO rasa.nlu.model - Starting to train component LexicalSyntacticFeaturizer
2021-08-06 18:57:58 INFO rasa.nlu.model - Finished training component.
2021-08-06 18:57:58 INFO rasa.nlu.model - Starting to train component CountVectorsFeaturizer
2021-08-06 18:57:58 INFO rasa.nlu.model - Finished training component.
2021-08-06 18:57:58 INFO rasa.nlu.model - Starting to train component CountVectorsFeaturizer
2021-08-06 18:57:58 INFO rasa.nlu.model - Finished training component.
2021-08-06 18:57:58 INFO rasa.nlu.model - Starting to train component DIETClassifier
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\nlu\classifiers\diet_classifier.py:656: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
model_data.add_features(key, sub_key, [np.array(_features)])
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Misaligned entity annotation in message 'what's my credit card account balance?' with intent 'check_balance'. Make sure the start and end values of entities in the training data match the token boundaries (e.g. entities don't include trailing whitespaces or punctuation).
More info at https://rasa.com/docs/rasa/training-data-format#nlu-training-data
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Misaligned entity annotation in message 'What's my a/c/c balance?' with intent 'check_balance'. Make sure the start and end values of entities in the training data match the token boundaries (e.g. entities don't include trailing whitespaces or punctuation).
More info at https://rasa.com/docs/rasa/training-data-format#nlu-training-data
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Misaligned entity annotation in message 'my acc balance' with intent 'check_balance'. Make sure the start and end values of entities in the training data match the token boundaries (e.g. entities don't include trailing whitespaces or punctuation).
More info at https://rasa.com/docs/rasa/training-data-format#nlu-training-data
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Misaligned entity annotation in message 'my acc.acccc. balance' with intent 'check_balance'. Make sure the start and end values of entities in the training data match the token boundaries (e.g. entities don't include trailing whitespaces or punctuation).
More info at https://rasa.com/docs/rasa/training-data-format#nlu-training-data
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Misaligned entity annotation in message 'my amount in account' with intent 'check_balance'. Make sure the start and end values of entities in the training data match the token boundaries (e.g. entities don't include trailing whitespaces or punctuation).
More info at https://rasa.com/docs/rasa/training-data-format#nlu-training-data
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Misaligned entity annotation in message 'check my credit score ' with intent 'check_creditscore'. Make sure the start and end values of entities in the training data match the token boundaries (e.g. entities don't include trailing whitespaces or punctuation).
More info at https://rasa.com/docs/rasa/training-data-format#nlu-training-data
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Misaligned entity annotation in message 'check my cibilcibil score' with intent 'check_creditscore'. Make sure the start and end values of entities in the training data match the token boundaries (e.g. entities don't include trailing whitespaces or punctuation).
More info at https://rasa.com/docs/rasa/training-data-format#nlu-training-data
Traceback (most recent call last):
File "c:\users\appstek v1\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\appstek v1\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\AppsTek V1\anaconda3\Scripts\rasa.exe\__main__.py", line 7, in <module>
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\__main__.py", line 116, in main
cmdline_arguments.func(cmdline_arguments)
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\cli\train.py", line 81, in train
return rasa.train(
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\train.py", line 43, in train
return rasa.utils.common.run_in_loop(
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\utils\common.py", line 308, in run_in_loop
result = loop.run_until_complete(f)
File "c:\users\appstek v1\anaconda3\lib\asyncio\base_events.py", line 616, in run_until_complete
return future.result()
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\train.py", line 102, in train_async
return await _train_async_internal(
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\train.py", line 198, in _train_async_internal
await _do_training(
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\train.py", line 240, in _do_training
model_path = await _train_nlu_with_validated_data(
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\train.py", line 541, in _train_nlu_with_validated_data
await rasa.nlu.train(
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\nlu\train.py", line 114, in train
interpreter = trainer.train(training_data, **kwargs)
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\nlu\model.py", line 204, in train
updates = component.train(working_data, self.config, **context)
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\nlu\classifiers\diet_classifier.py", line 771, in train
self.model.fit(
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\utils\tensorflow\models.py", line 184, in fit
) = self._get_tf_train_functions(eager, model_data, batch_strategy)
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\utils\tensorflow\models.py", line 425, in _get_tf_train_functions
self._get_tf_call_model_function(
File "c:\users\appstek v1\anaconda3\lib\site-packages\rasa\utils\tensorflow\models.py", line 408, in _get_tf_call_model_function
tf_call_model_function(next(iter(init_dataset)))
File "C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py", line 780, in __call__
result = self._call(*args, **kwds)
File "C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py", line 823, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py", line 696, in _initialize
self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
File "C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\function.py", line 2855, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\function.py", line 3213, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\function.py", line 3065, in _create_graph_function
func_graph_module.func_graph_from_py_func(
File "C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\framework\func_graph.py", line 986, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py", line 600, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\framework\func_graph.py", line 973, in wrapper
raise e.ag_error_metadata.to_exception(e)
NotImplementedError: in user code:
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\utils\tensorflow\models.py:257 train_on_batch *
prediction_loss = self.batch_loss(batch_in)
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\nlu\classifiers\diet_classifier.py:1608 batch_loss *
sequence_lengths = self._get_sequence_lengths(
c:\users\appstek v1\anaconda3\lib\site-packages\rasa\nlu\classifiers\diet_classifier.py:1588 _get_sequence_lengths *
sequence_lengths = tf.ones([batch_dim], dtype=tf.int32)
C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\util\dispatch.py:201 wrapper **
return target(*args, **kwargs)
C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\ops\array_ops.py:3041 ones
output = _constant_if_small(one, shape, dtype, name)
C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\ops\array_ops.py:2732 _constant_if_small
if np.prod(shape) < 1000:
<__array_function__ internals>:5 prod
C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\numpy\core\fromnumeric.py:3051 prod
return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,
C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\numpy\core\fromnumeric.py:86 _wrapreduction
return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
C:\Users\AppsTek V1\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\framework\ops.py:845 __array__
raise NotImplementedError(
NotImplementedError: Cannot convert a symbolic Tensor (strided_slice_6:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported