Bicleaner

* Update requirements for scikit-learn, scipy
* Fix training code for new scikit-learn version
* New Python version constraint
* New Bicleaner models trained with updated requirements

* Fix tests

* Fix requirements and installation

* Bicleaner hard-rules changes:
* Split hard-rules into a separated package.
* KenLM does not need to be installed when LM fluency filter isn't used.
* Update hardrules to 2.0, see [CHANGELOG](https://github.com/bitextor/bicleaner-hardrules/blob/6a7ea449c932ad8cf2a101e7841670f128335286/CHANGELOG.md).
* Bicleaner train changes:
* Qmax bug fixing.
* Classifier training uses the number of processes given by argument.
* Bicleaner classify changes:
* Refactored classifier scripts: code cleaning and remove lot of duplicated code.
* Buffered tokenization: improve speed of external tokenization tokenizing blocks of lines instead of line by line.

* Bicleaner hardrules changes:
* New rule: filter out sentences containing gluedWordsLikeThis.
* Rule change: Relaxed c_different_language rule for similar languages.
* New rule: filter out porn sentences using FastText classifier.
* Parameters changed: `-s/--source_lang` and `-t/--target_lang` are no longer mandatory (if a metadata .yaml file is provided)
* Bicleaner train changes:
* Default classifier is now `extra_trees`
* New parameters: `-f` and `-F`, source and target word frequency dictionaries.
* New qmax features:
* `qmax_nosmooth_nolimit_freq`: removes OOV smoothing, word limits and weights each target word with its monolingual probability using the word frequency dictionary.
* `qmax_nosmooth_nolimit_cummulated_prob_zipf_freq`: uses accumulated probability instead of maximum and splits the score into quartiles based on word frequencies.
* Added more bilingual dictionary coverage features, splitting them into quartiles based on monolingual word frequencies.
* Added new noise function that synthesizes negative samples cutting sentences and replacing words (this is not used by default, needs more testing).
* Changed classifier training behavior and use grid search.
* Removed `bicleaner_train_lite.py`
* Removed parameters: `-g` (`--good_examples`) and `-w` (`--wrong_examples`):
* Now, training automatically uses one half of the input file for good examples and the other half to synthesize wrong examples.
* Of this partitions, 90% will be used for training and the remaining 10% for testing.
* New parameter: `--relative_paths` allows to save model files paths relative instead of absolute (useful for training distributable models)
* Changed logging info messages, now more informative.
* Other
* Now using [sacremoses](https://github.com/alvations/sacremoses) instead of [mosestokenizer](https://github.com/luismsgomes/mosestokenizer)
* New script: `./utils/download-pack.sh` allows to download language packs for a given language pair.

* Bicleaner hardrules changes:
* Rule change: Relaxed c_minimal_length to accept 3-word sentences
* New feature: LM filtering (moved from Bicleaner Classify)
* New parameter: `--disable_lm_filter`, `--metadata` and `--lm_threshold`, to support LM filtering
* Bicleaner training changes:
* New parameter: Features relying on language identification can be disabled with flag `--disable_lang_ident` (this will be outputed in the .yaml file and used by Bicleaner clasifier)
* New feature: Debug mode now gives information on random forest feature importances
* Parameter change: --noisy_examples_file_sl and --noisy_examples_file_tl are now optional
* Parameter change: input now must be more than 10K sentences long
* Removed INFO messages when processes starting/ending (except when debugging)
* Bicleaner classifier changes:
* `--disable_lang_ident` flag is now read from the .yaml file
* Removed feature: LM filtering (moved to Bicleaner Hardrules)
* New parameter: `--disable_lm_filter`
* Removed parameters: `--keep_lm_result`, `--threshold`
* Other:
* Updated requirements