Ktrain

New:
- N/A

Changed
- Added `use_dynamic_shape` parameter to `text.preprocessor.hf_convert_examples` which is set to `True` when running predictions. This reduces the input length when making predictions, if possible..
- Added warnings to some imports in `imports.py` to allow for slightly lighter-weight deployments
- Temporarily pinning to `transformers>=3.1,<4.0` due to breaking changes in v4.0.

Fixed:
- Suppress progress bar in `predictor.predict` for `keras_bert` models
- Fixed typo causing problems when loading predictor for Inception models
- Fixes to address documented/undocumented breaking changes in `transformers>=4.0`. But, temporarily pinning to `transformers>=3.1,<4.0` for backwards compatibility.

New:
- The `SimpleQA.index_from_folder` method now supports text extraction from many file types including PDFs, MS Word documents, and MS PowerPoint files.

Changed
- The default in `SimpleQA.index_from_list` and `SimpleQA.index_from_folder` has been changed to `breakup_docs=True`.

Fixed:
- N/A

New:
- N/A

Changed
- `ktrain.text.textutils.extract_copy` now uses `textract` to extract text from many file types (e.g., PDF, DOC, PPT)
instead of just PDFs,

Fixed:
- N/A

New:
- N/A

Changed
- N/A

Fixed:
- Change exception in model ID check in `Translator` to warning to better allow offline language translations

New:
- `Predictor` instances now provide built-in support for exporting to TensorFlow Lite and ONNX.

Changed
- N/A


Fixed:
- N/A

New:
- N/A

Changed
- Use fast tokenizers for the following Hugging Face **transformers** models: BERT, DistilBERT, and RoBERTa models. This change affects models created with either `text.Transformer(...` or `text.text_clasifier('distilbert',..')`. BERT models created with `text_classifier('bert',..`, which uses `keras_bert` instead of `transformers`, are not affected by this change.


Fixed:
- N/A