Ktrain

New:
- Support for sequence tagging with Bidirectional LSTM-CRF. Word embeddings can currently be either
random or word2vec(cbow). If latter chosen, word vectors will be downloaded automaticlaly from Facebook fasttext
site.
- Added `ktra.text.texts_from_df` function

Changed:
- Added FutureWarning in text.text_classifier, that preproc will be required argument in future.
- In text.text_classifier, when preproc=None, use the maximum feature ID to populate max_features.


Fixed:
- Fixed construction of custom_objects dictionary for BERT to ensure load_model works for
custom BERT models
- Resolved issue with pretrained bigru models failing when max_features >= than total word count.

New:
- explain methods have been added to TextPredictor and ImagePredictor objects.
- TextPredictor.predict_proba and ImagePredictor.predict_proba_* convenience
methods have been added.
- Added utils.is_classifier utility function

Changed:
- TextPredictor.predict method can now accept a single document as input instead of
always requiring a list.
- Output of core.view_top_losses now includes the ground truth label of examples

Fixed:
- Fixed test of data loading

New:
- added additional tests of *ktrain*

Changed:
- Added classes argument to vision.images_from_folder. Only classes/subfolders
matching a name in the classes list will be considered.

Fixed:
- Resolved issue with using learner.view_top_losses with BERT models.

New:
- N/A

Changed:
- Added classes argument to vision.images_from_folder. Only classes/subfolders
matching a name in the classes list will be considered.

Fixed:
- Fixed issue with learner.validate and learner.predict failing when validation data is in
the form of an Iterator (e.g., DirectoryIterator).

New:
- N/A

Changed:
- Added check in ktrain.lroptimize.lrfinder to stop training if learning rate exceeds a fixed maximum,
which may happen when bad/dysfunctional model is supplied to learning rate finder.

Fixed:
- In ktrain.text.data.texts_from_folder function, only subfolders specified in classes argument
are read in as training and validation data.

New:
- N/A

Changed:
- N/A

Fixed:
- Fixed error related to validation_steps=None in call to fit_generator in ktrain.core on Google Colab.