Ktrain

New:
- N/A

Changed
- N/A/

Fixed:
- Added the `procs`, `limitmb`, and `multisegment` argumetns to `index_from_list` and `index_from_folder` method in `text.SimpleQA`
to speedup indexing when necessary. Supplying `multisegment=True` speeds things up significantly, for example. Defaults, however, are
the same as before. Users must explicitly change values if desiring a speedup.
- Load `xlm-roberta*` as `jplu/tf-xlm-roberta*` to bypass error from `transformers`

New:
- N/A

Changed
- [**breaking change**] The `multilabel` argument in `text.Transformer` constructor was moved to `Transformer.get_classifier` and now correctly allows
users to forcibly configure model for multilabel task regardless as to what data suggests. However, it is recommended to leave this value as `None`.
- The methods `predictor.save`, `ktrain.load_predictor`, `learner.save_model`, `learner.load_model` all now accept a path to folder where
all files (e.g., model file, `.preproc` file) will be saved. If path does not exist, it will be created.
This should not be a breaking change as the `load*` methods will still look for files in the old location if model or predictor was saved
using an older version of *ktrain*.

Fixed:
- N/A

New:
- N/A

Changed
- Added `n_samples` argument to `TextPredictor.explain` to address slowness of `explain` on Google Colab
- Lock to version 0.21.3 of `scikit-learn` to ensure old-style explanations are generated from `TextPredictor.explain`

Fixed:
- added missing `import pickle` to ensure saved topic models can be loaded

New:
- N/A

Changed
- Changed `Transformer.preprocess*` methods to accept sentence pairs for sentence pair classification

Fixed:
- N/A

New:
- Out-of-the-box support for image regression
- `vision.images_from_df` function to load image data from *pandas* DataFrames

Changed
- references to `fit_generator` and `predict_generator` converted to `fit` and `predict`

Fixed:
- Resolved issue with multilabel detection returning `False` for valid multilabel problems when data is in form of generator

New:
- Added `TFDataset` class for use as wrapper around arbitrary `tf.data.Dataset` objects for use in *ktrain*

Changed
- Added `NERPreprocessor.preprocess_train_from_conll2003`
- Removed extraneous imports from `text.__init__.py` and `vision.__init__.py`
- `classes` argument in `images_from_array` changed to `class_names`

Fixed:
- ensure NER data is properly prepared `text.ner.learner.validate`
- fixed typo with `df` reference in `images_from_fname`