not able to reproduce the training procedure

[ShrikanthSingh]

I referred to the website where I found a piece of code to reproduce training of Char2Vec but it produces an error, can you please help. If it works I want to train it for my purpose on the german text.
website: https://hackernoon.com/chars2vec-character-based-language-model-for-handling-real-world-texts-with-spelling-errors-and-a3e4053a147d

code:

import chars2vec

dim = 50

path_to_model = 'path/to/model/directory'

X_train = [('mecbanizing', 'mechanizing'), # similar words, target is equal 0
           ('dicovery', 'dis7overy'), # similar words, target is equal 0
           ('prot$oplasmatic', 'prtoplasmatic'), # similar words, target is equal 0
           ('copulateng', 'lzateful'), # not similar words, target is equal 1
           ('estry', 'evadin6'), # not similar words, target is equal 1
           ('cirrfosis', 'afear') # not similar words, target is equal 1
          ]
y_train = [0, 0, 0, 1, 1, 1]
model_chars = ['!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.',
               '/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '<',
               '=', '>', '?', '@', '_', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i',
               'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w',
               'x', 'y', 'z']
my_c2v_model = chars2vec.train_model(dim, X_train, y_train, model_chars)
chars2vec.save_model(my_c2v_model, path_to_model)
words = ['list', 'of', 'words']
c2v_model = chars2vec.load_model(path_to_model)
word_embeddings = c2v_model.vectorize_words(words)

Error:

ValueError                                Traceback (most recent call last)
<ipython-input-14-23a592d19001> in <module>
      1 # Create and train chars2vec model using given training data
----> 2 my_c2v_model = chars2vec.train_model(dim, X_train, y_train, model_chars)
      3 
      4 # Save pretrained model
      5 chars2vec.save_model(my_c2v_model, path_to_model)

C:\ProgramData\Anaconda3\lib\site-packages\chars2vec\model.py in train_model(emb_dim, X_train, y_train, model_chars, max_epochs, patience, validation_split, batch_size)
    235 
    236     targets = [float(el) for el in y_train]
--> 237     c2v_model.fit(X_train, targets, max_epochs, patience, validation_split, batch_size)
    238 
    239     return c2v_model

C:\ProgramData\Anaconda3\lib\site-packages\chars2vec\model.py in fit(self, word_pairs, targets, max_epochs, patience, validation_split, batch_size)
    105         x_2_pad_seq = keras.preprocessing.sequence.pad_sequences(x_2)
    106 
--> 107         self.model.fit([x_1_pad_seq, x_2_pad_seq], targets,
    108                        batch_size=batch_size, epochs=max_epochs,
    109                        validation_split=validation_split,

~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
    106   def _method_wrapper(self, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1037       # `Tensor` and `NumPy` input.
   1038       (x, y, sample_weight), validation_data = (
-> 1039           data_adapter.train_validation_split(
   1040               (x, y, sample_weight), validation_split=validation_split))
   1041 

~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\engine\data_adapter.py in train_validation_split(arrays, validation_split)
   1372   unsplitable = [type(t) for t in flat_arrays if not _can_split(t)]
   1373   if unsplitable:
-> 1374     raise ValueError(
   1375         "`validation_split` is only supported for Tensors or NumPy "
   1376         "arrays, found following types in the input: {}".format(unsplitable))

ValueError: `validation_split` is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'float'>, <class 'float'>, <class 'float'>, <class 'float'>, <class 'float'>, <class 'float'>]

I tried but even then not resolved

y_train = [0, 0, 0, 1, 1, 1]
y_train = np.array(y_train)

[ShrikanthSingh]

I referred to the website where I found a piece of code to reproduce training of Char2Vec but it produces an error, can you please help. If it works I want to train it for my purpose on the german text.
website: https://hackernoon.com/chars2vec-character-based-language-model-for-handling-real-world-texts-with-spelling-errors-and-a3e4053a147d

code:

import chars2vec

dim = 50

path_to_model = 'path/to/model/directory'

X_train = [('mecbanizing', 'mechanizing'), # similar words, target is equal 0
           ('dicovery', 'dis7overy'), # similar words, target is equal 0
           ('prot$oplasmatic', 'prtoplasmatic'), # similar words, target is equal 0
           ('copulateng', 'lzateful'), # not similar words, target is equal 1
           ('estry', 'evadin6'), # not similar words, target is equal 1
           ('cirrfosis', 'afear') # not similar words, target is equal 1
          ]
y_train = [0, 0, 0, 1, 1, 1]
model_chars = ['!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.',
               '/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '<',
               '=', '>', '?', '@', '_', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i',
               'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w',
               'x', 'y', 'z']
my_c2v_model = chars2vec.train_model(dim, X_train, y_train, model_chars)
chars2vec.save_model(my_c2v_model, path_to_model)
words = ['list', 'of', 'words']
c2v_model = chars2vec.load_model(path_to_model)
word_embeddings = c2v_model.vectorize_words(words)

Error:

ValueError                                Traceback (most recent call last)
<ipython-input-14-23a592d19001> in <module>
      1 # Create and train chars2vec model using given training data
----> 2 my_c2v_model = chars2vec.train_model(dim, X_train, y_train, model_chars)
      3 
      4 # Save pretrained model
      5 chars2vec.save_model(my_c2v_model, path_to_model)

C:\ProgramData\Anaconda3\lib\site-packages\chars2vec\model.py in train_model(emb_dim, X_train, y_train, model_chars, max_epochs, patience, validation_split, batch_size)
    235 
    236     targets = [float(el) for el in y_train]
--> 237     c2v_model.fit(X_train, targets, max_epochs, patience, validation_split, batch_size)
    238 
    239     return c2v_model

C:\ProgramData\Anaconda3\lib\site-packages\chars2vec\model.py in fit(self, word_pairs, targets, max_epochs, patience, validation_split, batch_size)
    105         x_2_pad_seq = keras.preprocessing.sequence.pad_sequences(x_2)
    106 
--> 107         self.model.fit([x_1_pad_seq, x_2_pad_seq], targets,
    108                        batch_size=batch_size, epochs=max_epochs,
    109                        validation_split=validation_split,

~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
    106   def _method_wrapper(self, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1037       # `Tensor` and `NumPy` input.
   1038       (x, y, sample_weight), validation_data = (
-> 1039           data_adapter.train_validation_split(
   1040               (x, y, sample_weight), validation_split=validation_split))
   1041 

~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\engine\data_adapter.py in train_validation_split(arrays, validation_split)
   1372   unsplitable = [type(t) for t in flat_arrays if not _can_split(t)]
   1373   if unsplitable:
-> 1374     raise ValueError(
   1375         "`validation_split` is only supported for Tensors or NumPy "
   1376         "arrays, found following types in the input: {}".format(unsplitable))

ValueError: `validation_split` is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'float'>, <class 'float'>, <class 'float'>, <class 'float'>, <class 'float'>, <class 'float'>]

I found the solution by editing the code snippet in model.py:
from: targets = [float(el) for el in y_train]
to: targets = np.array(y_train)

But Authors and collaborators please confirm if this is right. Because I will be using it for a crucial project.

[ZaxR]

I've hit the same error. Any chance we can get a fix for this? Thanks!

[ShrikanthSingh]

I've hit the same error. Any chance we can get a fix for this? Thanks!
Try this:
I found the solution by editing the code snippet in model.py:
from: targets = [float(el) for el in y_train]
to: targets = np.array(y_train)

[gab-palmeri]

Can we have this fix pushed in the pip repository, so that we don't have to manually download this project?