2.3.4

[TFA-MAIF]

New features:

PR 128: A Lemmatizer class has been added by the Société Générale team! Lemmatizer object is compatible with sklearn pipelines and is built around an sklearn Transformer. Details can be found in tutorial 04 and 08
PR 132: A Stemmerclass has been added. Details can also be found in tutorial 04
PR 132: A DeterministicEmojiFlaggerclass has been added to flag emojis. Details can be found in tutorial 08.

Updates:

Python 3.6 is no longer supported for tensorflow compatibility issues. Melusine is now running with Tensorflow 2.8
PR 121: Add the return of the histogram after the training (train.py)
PR 120: Tokenizer can now be specified in a NeuralModel init. Embedding and Phraser classes have been simplified. See tutorial 04
PR 120: Config has been split into different functionalities files that can be found in /melusine/config/parameters for more readability. See tutorial 10
PR 120: A text_flaggerand a token_flagger class have been created to give you a glimpse of the library redesign but are not called yet.

Bug fix:

PR 124: fixing purge of dict_attr keys while saving bert models (train.py)
Issue 126: fixing initialisation of bert_tokenizer for cross validation (train.py)

---

This discussion was created from the release 2.3.4.