mikmum / transformer-conll

Adam Mickiewicz University Deep Learning course, Summer Semester 2024

CoNLL-2003 English Named Entity Recognition.

NER challenge for CoNLL-2003 English. Annotations were taken from University of Antwerp. The English data is a collection of news wire articles from the Reuters Corpus, RCV1.

Format of the train set

The train set has just two columns separated by TABs:

• the expected BIO labels,
• the docuemnt.

Each line is a separate training item. Note that this is TSV format, not CSV, double quotes are not interpreted in a special way!

Preprocessing snippet located here

End-of-lines inside documents were replaced with the '' tag.

The train is compressed with the xz compressor, in order to see a random sample of 10 training items, run:

xzcat train/train.tsv.xz | shuf -n 10 | less -S

(The -S disables line wrapping, press "q" to exit less browser.)

Format of the test sets

For the test sets, the input data is given in two files: the text in in.tsv and the expected labels in expected.tsv. (The files have .tsv extensions for consistency but actually they do not contain TABs.)

To see the first 5 test items run:

cat dev-0/in.tsv | paste dev-0/expected.tsv - | head -n 5

The expected.tsv file for the test-A test set is hidden and is not available in the master branch.

Evaluation metrics

One evaluation metric is used:

• BIO-F1

Directory structure

• README.md — this file
• config.txt — GEval configuration file
• train/ — directory with training data
• train/train.tsv.xz — train set
• dev-0/ — directory with dev (test) data (split preserved from CoNLL-2003)
• dev-0/in.tsv — input data for the dev set
• dev-0/expected.tsv — expected (reference) data for the dev set
• test-A — directory with test data
• test-A/in.tsv — input data for the test set
• test-A/expected.tsv — expected (reference) data for the test set (hidden from the developers, not available in the master branch)

Usually, there is no reason to change these files.