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Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction

This repository contains the code of our paper:
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction
Christoph Alt, Marc Hübner, Leonhard Hennig

Our code depends on huggingface's PyTorch reimplementation of the OpenAI GPT, and AllenNLP - so thanks to them.

The code is tested with:

• Python 3.6.6
• PyTorch 1.0.1
• AllenNLP 0.7.1

Installation

First, clone the repository to your machine and install the requirements with the following command:

pip install -r requirements.txt

Second, download the OpenAI GPT archive (containing all model related files):

wget -O openai-finetune-lm.tar.gz "https://dfkide-my.sharepoint.com/:u:/g/personal/lehe02_dfki_de/EUzPdbmAk8BMoM_fFDsMiJ4BHawtbZLMgIZQWTyMa4csdQ?Web=0&Download=1"

The model should be placed in the root folder of the project.

Note: The original model params are available here: https://github.com/openai/finetune-transformer-lm/tree/master/model . You can also download from there and then create a single archive file with:

tar cvzf openai-finetune-lm.tar.gz model/

Prepare the data

We evaluate our model on the NYT dataset and use the version provided by OpenNRE.

Since the original data is not available anymore as JSON on OpenNRE, please download our cached copy:

wget -O opennre-nyt10.tgz "https://dfkide-my.sharepoint.com/:u:/g/personal/lehe02_dfki_de/EcCAEQhsKYNHlqoFq8m-3HgBbllwovMmjZjozqyTylh52w?Web=0&Download=1"
tar xvzf opennre-nyt10.tgz

This creates a folder "data/open_nre_nyt" containing train.json and test.json.

Outdated: Follow the OpenNRE instructions for creating the NYT dataset in JSON format:

1. download the nyt.tar file.
2. extract the archive with: tar -xvf nyt.tar
3. create the protobuf files: protoc --proto_path=. --python_out=. Document.proto
4. convert the protobuf files to json: python protobuf2json.py .
5. move train.json and test.json to data/open_nre_nyt/

Training

E.g. for training on the NYT dataset, run the following command:

CUDA_VISIBLE_DEVICES=0 allennlp train \
    experiments/configs/model_paper.json \
    -s <MODEL AND METRICS DIR> \
    --include-package tre

Evaluation

CUDA_VISIBLE_DEVICES=0 python ./experiments/utils/pr_curve_and_predictions.py \
    <MODEL AND METRICS DIR> \
    ./data/open_nre_nyt/test.json \
    --output-dir <RESULTS DIR> \
    --archive-filename <MODEL ARCHIVE FILENAME>

Trained Models

The model(s) we trained on NYT to produce our paper results can be found here:

Download and extract model files

Download the archive corresponding to the model you want to evaluate (links in the table above).

wget -O model_lm05_wu2_do2_bs16_att.tar.gz "https://dfkide-my.sharepoint.com/:u:/g/personal/lehe02_dfki_de/ESnHZWbh-KtLgS8XVFeQbtIBTHVDn7u3Ekw7u6ysmgzvSw?Web=0&Download=1"

Run evaluation

For example, to evaluate the NYT model used in the paper, run the following command:

CUDA_VISIBLE_DEVICES=0 python ./experiments/utils/pr_curve_and_predictions.py \
    <DIR CONTAINING THE MODEL ARCHIVE> \
    ./data/open_nre_nyt/test.json \
    --output-dir ./results/ \
    --archive-filename model_lm05_wu2_do2_bs16_att.tar.gz

Citations

If you use our code in your research or find our repository useful, please consider citing our work.

@inproceedings{alt-etal-2019-fine,
    title = "Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction",
    author = {Alt, Christoph  and
      H{\"u}bner, Marc  and
      Hennig, Leonhard},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1134",
    pages = "1388--1398",
}

License

DISTRE is released under the Apache 2.0 license. See LICENSE for additional details.