Ktrain

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

New:
- N/A

Changed
- N/A


Fixed:
- Resolved issue in `qa.ask` method occuring with embedding computations when full answer sentences exceed 512 tokens.

New:
- Support for upcoming release of TensorFlow 2.4 such as removal of references to obsolete `multi_gpu_model`

Changed
- **[breaking change]** `TopicModel.get_docs` now returns a list of dicts instead of a list of tuples. Each dict has keys: `text`, `doc_id`, `topic_proba`, `topic_id`.
- added `TopicModel.get_document_topic_distribution`
- added `TopicModel.get_sorted_docs` method to return all documents sorted by relevance to a given `topic_id`


Fixed:
- Changed version check warning in `lr_find` to a raised Exception to avoid confusion when warnings from **ktrain** are suppressed
- Pass `verbose` parameter to `hf_convert_examples`

New:
- N/A

Changed
- changed `qa.core.display_answers` to make URLs open in new tab


Fixed:
- pin to `seqeval==0.0.19` due to `numpy` version incompatibility with latest TensorFlow and to suppress errors during installation

New:
- N/A

Changed
- N/A


Fixed:
- fixed issue with missing noun phrase at end of sentence in `extract_noun_phrases`
- fixed TensorFlow versioning issues with `utils.metrics_from_model`

New:
- added `extract_noun_phrases` to `textutils`

Changed
- `SimpleQA.ask` now includes an `include_np` parameter. When True, noun phrases will be used to retrieve documents
containing candidate answers.


Fixed:
- N/A