Named Entity Recognition

Based on the scripts run_ner.py for Pytorch and run_tf_ner.py for Tensorflow 2. This example fine-tune Bert Multilingual on GermEval 2014 (German NER). Details and results for the fine-tuning provided by @stefan-it.

Data (Download and pre-processing steps)

Data can be obtained from the GermEval 2014 shared task page.

Here are the commands for downloading and pre-processing train, dev and test datasets. The original data format has four (tab-separated) columns, in a pre-processing step only the two relevant columns (token and outer span NER annotation) are extracted:

curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-train.tsv?attredirects=0&d=1' \
| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > train.txt.tmp
curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-dev.tsv?attredirects=0&d=1' \
| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > dev.txt.tmp
curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-test.tsv?attredirects=0&d=1' \
| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > test.txt.tmp

The GermEval 2014 dataset contains some strange "control character" tokens like '\x96', '\u200e', '\x95', '\xad' or '\x80'. One problem with these tokens is, that BertTokenizer returns an empty token for them, resulting in misaligned InputExamples. I wrote a script that a) filters these tokens and b) splits longer sentences into smaller ones (once the max. subtoken length is reached).

wget "https://raw.githubusercontent.com/stefan-it/fine-tuned-berts-seq/master/scripts/preprocess.py"

Let's define some variables that we need for further pre-processing steps and training the model:

export MAX_LENGTH=128
export BERT_MODEL=bert-base-multilingual-cased

Run the pre-processing script on training, dev and test datasets:

python3 preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
python3 preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
python3 preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt

The GermEval 2014 dataset has much more labels than CoNLL-2002/2003 datasets, so an own set of labels must be used:

cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt

Prepare the run

Additional environment variables must be set:

export OUTPUT_DIR=germeval-model
export BATCH_SIZE=32
export NUM_EPOCHS=3
export SAVE_STEPS=750
export SEED=1

Run the Pytorch version

To start training, just run:

python3 run_ner.py --data_dir ./ \
--model_type bert \
--labels ./labels.txt \
--model_name_or_path $BERT_MODEL \
--output_dir $OUTPUT_DIR \
--max_seq_length  $MAX_LENGTH \
--num_train_epochs $NUM_EPOCHS \
--per_gpu_train_batch_size $BATCH_SIZE \
--save_steps $SAVE_STEPS \
--seed $SEED \
--do_train \
--do_eval \
--do_predict

If your GPU supports half-precision training, just add the --fp16 flag. After training, the model will be both evaluated on development and test datasets.

Evaluation

Evaluation on development dataset outputs the following for our example:

10/04/2019 00:42:06 - INFO - __main__ -   ***** Eval results  *****
10/04/2019 00:42:06 - INFO - __main__ -     f1 = 0.8623348017621146
10/04/2019 00:42:06 - INFO - __main__ -     loss = 0.07183869666975543
10/04/2019 00:42:06 - INFO - __main__ -     precision = 0.8467916366258111
10/04/2019 00:42:06 - INFO - __main__ -     recall = 0.8784592370979806

On the test dataset the following results could be achieved:

10/04/2019 00:42:42 - INFO - __main__ -   ***** Eval results  *****
10/04/2019 00:42:42 - INFO - __main__ -     f1 = 0.8614389652384803
10/04/2019 00:42:42 - INFO - __main__ -     loss = 0.07064602487454782
10/04/2019 00:42:42 - INFO - __main__ -     precision = 0.8604651162790697
10/04/2019 00:42:42 - INFO - __main__ -     recall = 0.8624150210424085

Comparing BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased)

Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased) with the same hyperparameters as specified in the example documentation (one run):

Model	F-Score Dev	F-Score Test
bert-large-cased	95.59	91.70
roberta-large	95.96	91.87
distilbert-base-uncased	94.34	90.32

Run PyTorch version using PyTorch-Lightning

Run bash run_pl.sh from the ner directory. This would also install pytorch-lightning and the examples/requirements.txt. It is a shell pipeline which would automatically download, pre-process the data and run the models in germeval-model directory. Logs are saved in lightning_logs directory.

Pass --n_gpu flag to change the number of GPUs. Default uses 1. At the end, the expected results are: TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recall': 0.869537067011978, 'f1': 0.8608974358974358}

Run the Tensorflow 2 version

To start training, just run:

python3 run_tf_ner.py --data_dir ./ \
--model_type bert \
--labels ./labels.txt \
--model_name_or_path $BERT_MODEL \
--output_dir $OUTPUT_DIR \
--max_seq_length  $MAX_LENGTH \
--num_train_epochs $NUM_EPOCHS \
--per_device_train_batch_size $BATCH_SIZE \
--save_steps $SAVE_STEPS \
--seed $SEED \
--do_train \
--do_eval \
--do_predict

Such as the Pytorch version, if your GPU supports half-precision training, just add the --fp16 flag. After training, the model will be both evaluated on development and test datasets.

Evaluation

Evaluation on development dataset outputs the following for our example:

           precision    recall  f1-score   support

 LOCderiv     0.7619    0.6154    0.6809        52
  PERpart     0.8724    0.8997    0.8858      4057
  OTHpart     0.9360    0.9466    0.9413       711
  ORGpart     0.7015    0.6989    0.7002       269
  LOCpart     0.7668    0.8488    0.8057       496
      LOC     0.8745    0.9191    0.8963       235
 ORGderiv     0.7723    0.8571    0.8125        91
 OTHderiv     0.4800    0.6667    0.5581        18
      OTH     0.5789    0.6875    0.6286        16
 PERderiv     0.5385    0.3889    0.4516        18
      PER     0.5000    0.5000    0.5000         2
      ORG     0.0000    0.0000    0.0000         3

micro avg     0.8574    0.8862    0.8715      5968
macro avg     0.8575    0.8862    0.8713      5968

On the test dataset the following results could be achieved:

           precision    recall  f1-score   support

  PERpart     0.8847    0.8944    0.8896      9397
  OTHpart     0.9376    0.9353    0.9365      1639
  ORGpart     0.7307    0.7044    0.7173       697
      LOC     0.9133    0.9394    0.9262       561
  LOCpart     0.8058    0.8157    0.8107      1150
      ORG     0.0000    0.0000    0.0000         8
 OTHderiv     0.5882    0.4762    0.5263        42
 PERderiv     0.6571    0.5227    0.5823        44
      OTH     0.4906    0.6667    0.5652        39
 ORGderiv     0.7016    0.7791    0.7383       172
 LOCderiv     0.8256    0.6514    0.7282       109
      PER     0.0000    0.0000    0.0000        11

micro avg     0.8722    0.8774    0.8748     13869
macro avg     0.8712    0.8774    0.8740     13869