Ktrain

New:
- support for language translation using pretraiend `MarianMT` models
- added `core.evaluate` as alias to `core.validate`
- `Learner.estimate_lr` method will return numerical estimates of learning rate using two different methods.
Should only be called **after** running `Learner.lr_find`.

Changed
- `text.zsl.ZeroShotClassifier` changed to use `AutoModel*` and `AutoTokenizer` in order to load any `mlni` model
- remove external modules from `ktrain.__init__.py` so that they do not appear when pressing TAB in notebook
- added `Transformer.save_tokenizer` and `Transformer.get_tokenizer` methods to facilitate training on machines
with no internet

Fixed:
- explicitly call `plt.show()` in `LRFinder.plot_loss` to resolved issues with plot not displaying in certain cases (PR 170)
- suppress warning about text regression when making text regression predictions
- allow `xnli` models for `zsl` module

New:
- added `metrics` parameter to `text.text_classifier` and `text.text_regression_model` functions
- added `metrics` parameter to `Transformer.get_classifier` and `Transformer.get_regrssion_model` methods

Changed
- `metric` parameter in `vision.image_classifier` and `vision.image_regression_model` functions changed to `metrics`

Fixed:
- N/A

New:
- N/A

Changed
- default model for summarization changed to `facebook/bart-large-cnn` due to breaking change in v2.11
- added `device` argument to `TransformerSummarizer` constructor to control PyTorch device

Fixed:
- require `transformers>=2.11.0` due to breaking changes in v2.11 related to `BART` models

New:
- N/A

Changed
- N/A/

Fixed:
- prevent `transformer` tokenizers from being pickled during `predictor.save`, as it causes problems for
some community-uploaded models like `bert-base-japanese-whole-word-masking`.

New:
- support for Zero-Shot Topic Classification via the `text.ZeroShotClassifier`.

Changed
- N/A/

Fixed:
- N/A

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`