Hi, I am migrating my bot from 1.x to 2.x, and I followed the tutorials here: Migrating your Rasa 1.x assistant to Rasa 2.0
I didn’t go in and change any files, because I’m still confused about how I should change forms. I tried training the bot and am getting an error:
c:\users\user\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Intent ‘nlu_fallback’ has only 1 training examples! Minimum is 2, training may fail.
c:\users\user\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Intent ‘user_Name’ has only 1 training examples! Minimum is 2, training may fail.
c:\users\user\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Intent ‘user_doorNo’ has only 1 training examples! Minimum is 2, training may fail.
c:\users\user\anaconda3\lib\site-packages\rasa\shared\utils\io.py:93: UserWarning: Intent ‘user_street’ has only 1 training examples! Minimum is 2, training may fail.
2020-12-21 18:03:18 INFO rasa.nlu.model - Starting to train component HFTransformersNLP
Traceback (most recent call last):
File “c:\users\user\anaconda3\lib\runpy.py”, line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File “c:\users\user\anaconda3\lib\runpy.py”, line 87, in _run_code
exec(code, run_globals)
File “C:\Users\USER\anaconda3\Scripts\rasa.exe_main_.py”, line 7, in
File “c:\users\user\anaconda3\lib\site-packages\rasa_main_.py”, line 116, in main
cmdline_arguments.func(cmdline_arguments)
File “c:\users\user\anaconda3\lib\site-packages\rasa\cli\train.py”, line 58, in
train_parser.set_defaults(func=lambda args: train(args, can_exit=True))
File “c:\users\user\anaconda3\lib\site-packages\rasa\cli\train.py”, line 90, in train
training_result = rasa.train(
File “c:\users\user\anaconda3\lib\site-packages\rasa\train.py”, line 94, in train
return rasa.utils.common.run_in_loop(
File “c:\users\user\anaconda3\lib\site-packages\rasa\utils\common.py”, line 308, in run_in_loop
result = loop.run_until_complete(f)
File “c:\users\user\anaconda3\lib\asyncio\base_events.py”, line 616, in run_until_complete
return future.result()
File “c:\users\user\anaconda3\lib\site-packages\rasa\train.py”, line 163, in train_async
return await _train_async_internal(
File “c:\users\user\anaconda3\lib\site-packages\rasa\train.py”, line 342, in _train_async_internal
await _do_training(
File “c:\users\user\anaconda3\lib\site-packages\rasa\train.py”, line 388, in _do_training
model_path = await _train_nlu_with_validated_data(
File “c:\users\user\anaconda3\lib\site-packages\rasa\train.py”, line 811, in _train_nlu_with_validated_data
await rasa.nlu.train(
File “c:\users\user\anaconda3\lib\site-packages\rasa\nlu\train.py”, line 116, in train
interpreter = trainer.train(training_data, **kwargs)
File “c:\users\user\anaconda3\lib\site-packages\rasa\nlu\model.py”, line 209, in train
updates = component.train(working_data, self.config, **context)
File “c:\users\user\anaconda3\lib\site-packages\rasa\nlu\utils\hugging_face\hf_transformers.py”, line 720, in train
batch_docs = self._get_docs_for_batch(batch_messages, attribute)
File “c:\users\user\anaconda3\lib\site-packages\rasa\nlu\utils\hugging_face\hf_transformers.py”, line 670, in _get_docs_for_batch
) = self._get_model_features_for_batch(
File “c:\users\user\anaconda3\lib\site-packages\rasa\nlu\utils\hugging_face\hf_transformers.py”, line 602, in _get_model_features_for_batch
sequence_hidden_states = self._compute_batch_sequence_features(
File “c:\users\user\anaconda3\lib\site-packages\rasa\nlu\utils\hugging_face\hf_transformers.py”, line 456, in _compute_batch_sequence_features
model_outputs = self.model(
File “C:\Users\USER\AppData\Roaming\Python\Python38\site- packages\tensorflow\python\keras\engine\base_layer.py”, line 1012, in call
outputs = call_fn(inputs, *args, **kwargs)
File “C:\Users\USER\AppData\Roaming\Python\Python38\site- packages\transformers\models\bert\modeling_tf_bert.py”, line 844, in call
inputs = input_processing(
File “C:\Users\USER\AppData\Roaming\Python\Python38\site- packages\transformers\modeling_tf_utils.py”, line 357, in input_processing
raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.")
ValueError: Data of type <class ‘numpy.ndarray’> is not allowed only (<class ‘tensorflow.python.framework.ops.Tensor’>, <class ‘bool’>, <class ‘int’>, <class ‘transformers.file_utils.ModelOutput’>, <class ‘tuple’>, <class ‘list’>, <class ‘dict’>) is accepted for attention_mask.
Help?
