fairseq-tagging: A Python repository from SeolhwaLee

a Fairseq fork 🍴 adapted for sequence tagging/labeling tasks (NER, POS Tagging, etc)

Motivation

Fairseq is a great library to build sequence-to-sequence models. Unfortunately, it does not support sequence labeling tasks, and you will need to treat the task as seq2seq to make use of Fairseq. This will deprive you of fine-tuning pre-trained models such as RoBERTa XLM-R and BERT and will require you to needlessly train an extra decoder network. I adapted Fairseq here for these tasks so that one is able to utilize the full power of fairseq when training on these tasks.

Example: Training tiny BERT on NER (from scratch) on CoNLL-2003

1. Prepare Data

Assumming your data is in the following IOB format:

SOCCER NN B-NP O 
JAPAN NNP B-NP B-LOC
GET VB B-VP O
LUCKY NNP B-NP O
WIN NNP I-NP O
, , O O

CHINA NNP B-NP B-PER
IN IN B-PP O
SURPRISE DT B-NP O
DEFEAT NN I-NP O
. . O O

with the 3 splits train, valid and test in path/to/data/conll-2003

Run

python preprocess.py --seqtag-data-dir path/to/data/conll-2003 \
      --destdir path/to/data/conll-2003 \
      --nwordssrc 30000 \
      --bpe sentencepiece \
      --sentencepiece-model /path/to/sentencepiece.bpe.model

2. Train

Let's train a tiny BERT (L=2, D=128, H=2) model from scratch:

python train.py data/conll-2003/bin \ 
      --arch bert_sequence_tagger_tiny \
      --criterion sequence_tagging \
      --max-sentences 16  \
      --task sequence_tagging \
      --max-source-positions 128 \
      -s source.bpe \
      -t target.bpe \
      --no-epoch-checkpoints \
      --lr 0.005 \
      --optimizer adam \
      --clf-report \
      --max-epoch 20 \
      --best-checkpoint-metric F1-score \
      --maximize-best-checkpoint-metric

Training starts:

epoch 001 | loss 2.313 | ppl 4.97 | F1-score 0 | wps 202.2 | ups 9.09 | wpb 18 | bsz 1.5 | num_updates 2 | lr 0.005 | gnorm 4.364 | clip 0 | train_wall 0 | wall 0                            
epoch 002 | valid on 'valid' subset | loss 0.557 | ppl 1.47 | F1-score 0.666667 | wps 549.4 | wpb 18 | bsz 1.5 | num_updates 4 | best_F1-score 0.666667                                       
epoch 002:   0%|                                                                                                                                                        | 0/2 [00:00<?, ?it/s]2020-06-05 22:09:03 | INFO | fairseq.checkpoint_utils | saved checkpoint checkpoints/checkpoint_best.pt (epoch 2 @ 4 updates, score 0.6666666666666666) (writing took 0.09897447098046541 seconds)
epoch 002 | loss 1.027 | ppl 2.04 | F1-score 0 | wps 121.8 | ups 6.77 | wpb 18 | bsz 1.5 | num_updates 4 | lr 0.005 | gnorm 2.657 | clip 0 | train_wall 0 | wall 1  
...

3. Predict and Evaluate

python predict.py path/to/data/conll-2003/bin \
         --path checkpoints/checkpoint_last.pt \
         --task sequence_tagging \
         -s source.bpe -t target.bpe \
         --pred-subset test
         --results-path model_outputs/

This writes source and prediction to model_outputs/test.txt and prints:

    precision    recall  f1-score   support

     PERS     0.7156    0.7506    0.7327       429
      ORG     0.5285    0.5092    0.5187       273
      LOC     0.7275    0.7105    0.7189       342

micro avg     0.6724    0.6743    0.6734      1044
macro avg     0.6706    0.6743    0.6722      1044

TODO