Data & Analytics Research
The Part Of Speech Tagging (POSTagging) is the process of labeling a word in a text (corpus) with it's corresponding particular part of speech. This implementation uses BiLSTM-CRF for solving the POSTagging task utilizing PyTorch framework for training a supervised model and predicting in CPU. For training it receives a pre-trained FastText Gensim embedding and a .csv file. It outputs three pickle files: model, word vocabulary and label vocabulary.
Here are presented these content:
This version works in:
- PyTorch: 1.7.1
- Python: 3.6
Install all required packages (other than pytorch) from requirements.txt
pip install -r requirements.txt
Prepare data first. Data must be supplied in one csv file where the first column contain the sentences and the second one the respective labels for that sentence. File might be prepared as follows:
(sample.csv)
MessageProcessed, Tags
quero o meu boleto, VERB ART PRON SUBS
não consegui contato por telefone, ADV VERB SUBS PREP SUBS
..., ...
Then the above input is provided to train.py using --input-path and the column name for the sentences and the labels using --sentence_column and --label_column.
python train.py --input-path *.csv --sentence_column MessageProcessed --label_column Tags ...
You might need to setup several more parameters in order to make it work.
A few parameters available on training are:
--batch-size: number of sentences in each batch.--epochs: number of epochs--learning_rate: learning rate parameter value
And parameters for validation and early stopping.
For local execution run command:
python train.py --input-path *.csv --separator , --sentence_column MessageProcessed --label_column Tags --save-dir * --wordembed-path *.kv --epochs 5
python train.py --input-path *.csv --separator , --sentence_column MessageProcessed --label_column Tags --save-dir * --wordembed-path f*.kv --epochs 5 --val --val-path *.csv --bidirectional --val-period 1e
python train.py --input-path *.csv --separator , --sentence_column MessageProcessed --label_column Tags --save-dir * --wordembed-path *.kv --epochs 5 --val --val-path *.csv --bidirectional --val-period 10i --max-decay-num 2 --max-patience 2 --learning-rate-decay 0.1 --patience-threshold 0.98
For local execution run command for one line predict:
python predict.py --model-path *.pkl --input-sentence "eu quero prever essa frase" --label-vocab *.pkl --save-dir *.csv --wordembed-path *.kv
For local execution run command for batch predict:
python predict.py --model-path *.pkl --input-path *.csv --sentence_column MessageProcessed --label-vocab *.pkl --save-dir *.csv --wordembed-path *.kv
python predict.py --model-path *.pkl --input-path *.csv --sentence_column MessageProcessed --label-vocab *.pkl --save-dir *.csv --wordembed-path *.kv --use-lstm-output
Data must be supplied in one csv file with one column which contain the sentences. File might be prepared as follows:
(sample.csv)
MessageProcessed
quero o meu boleto
não consegui contato por telefone
...,