onto_to_spacy_json.py reads in a directory of OntoNotes 5 annotations and
creates training data in spaCy's
JSON input for training. The
program currently only gets NER tags, ignoring POS (and dependency, which is
not natively in OntoNotes).
Use it like this: (if you pip install the spacy model)
python onto_to_spacy_json.py -i "ontonotes-release-5.0/data/arabic/annotations/nw/ann/00" -t "ar_train.json" -e "ar_eval.json" -v 0.1
Use it like this to train arabic ner model
python -m spacy train ar ar_test_output_all ar_train_all.json ar_eval_all.json --no-tagger --no-parser
In order to load the model and use it take a look at the file:
test_spacy_model.ipynb
Use it like this:(if you customozied build the model for me is v2.0.9 , the difference is you need to give thd dir of the output model)
python -m spacy train ar /Users/yanliang/arabicNer/data/ar_output_all /Users/yanliang/arabicNer/data/ar_train_all.json /Users/yanliang/arabicNer/data/ar_eval_all.json --no-tagger --no-parser```
rehearsal.py is a script that generates a new Prodigy dataset containing both
NER labeled examples from a given dataset, as well as a number of OntoNotes
examples per annotation. Mixing in old gold standard annotations prevents
catastrophic forgetting.
For example, the following will augment the annotations in the loc_ner_db
dataset with OntoNotes annotations:
python rehearsal.py "loc_ner_db" 5
The augmented data is written to a dataset called augmented_for_training,
which should be treated as temporary because the script overwrites it each
time. NER training can then be performed as usual:
prodigy ner.batch-train augmented_for_training en_core_web_sm --eval-split 0.2
First of all, if you don't have prodigy on your local, you need to install it, and create a db (sqlite by default) for where to import your prodigy data:
python3 -m prodigy dataset arabicner "train arabic ner"
python3 -m prodigy db-in arabicner single_arabic_ner.jsonl
reheasal your dataset with onto_notes, the dir for onto_notes data is hard coded in rehearsal.py, you need to edit from there (5 here means that 5* onto_notes many records will be mixed in prodigy data)
python3 rehearsal.py "arabicner" 5
python3 -m prodigy ner.batch-train augmented_for_training /home/yan/arabicner/Arabic-NER/testmodel/model8 --eval-split 0.2
if you want to explore the sqlitedb for prodigy, you need to go to your home directory and do sqlite3 .prodigy/prodigy.db it has "dataset", "example", "link" tables, and your data will be under example table.
the pretrained vector model could be too big for the training process to process, so we can prune the huge vector first then use it
python3 generatePruningVectorModel.py -l ar -v 0.0.0
(version is needed for later training, since it iwll look at that field), the directory of the .vec is hard coded in the code
then you will get a language model with a pretrained pruned vectors, then you use this model to train you ner model with this command.
python3 -m spacy train ar /home/yan/arabicNER/Arabic-NER/experiments/exp2/ar_output_all /home/yan/arabicNER/Arabic-NER/data/combined.json /home/yan/arabicNER/Arabic-NER/data/ar_eval_all.json --no-tagger --no-parser --vectors "/the_model_you_just_got"
after that you will get several ner model as output and say model0 , if you make the training now with this command ,
python3 -m prodigy ner.batch-train augmented_for_training /nermodel --eval-split 0.2
spacy will throw error, it does not like the /vocab defined in this ner model. it throws exception. but what I did is inside of ner model. it has a ner directory, you can copy this ner directory to the pruned-language model, and then update its meta.json under the directory, then make prodigy ner.batch-train looking at the language model (add in ner directory and udpated meta.json) it will succesfully run in this way.
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