LAMA: LAnguage Model Analysis

This repository is forked from https://github.com/facebookresearch/LAMA

LAMA is a probe for analyzing the factual and commonsense knowledge contained in pretrained language models.

The dataset for the LAMA probe is available at https://dl.fbaipublicfiles.com/LAMA/data.zip

LAMA contains a set of connectors to pretrained language models.
LAMA exposes a transparent and unique interface to use:

Transformer-XL (Dai et al., 2019)
BERT (Devlin et al., 2018)
ELMo (Peters et al., 2018)
GPT (Radford et al., 2018)
RoBERTa (Liu et al., 2019)

Actually, LAMA is also a beautiful animal.

The LAMA probe

To reproduce the results:

1. Create conda environment and install requirements

Use a separate conda environment. It can be created by running:

conda create -n lama37 -y python=3.7 && conda activate lama37
pip install -r requirements.txt

2. Download the data

wget https://dl.fbaipublicfiles.com/LAMA/data.zip
unzip data.zip
rm data.zip

3. Download the models

Disk usage: ~55 GB.

Install spacy model

python3 -m spacy download en

Download the models

chmod +x download_models.sh
./download_models.sh

The script will create and populate a pre-trained_language_models folder. If you are interested in a particular model please edit the script.

4. Run the experiments

chmod +x run_experiment/*.sh

Run cased model

./run_experiment/run_experiment_cased.sh

Run uncased model

./run_experiment/run_experiment_uncased.sh

results will be logged in output/ and run_experiment/*.log.

Other versions of LAMA

LAMA-UHN

This repository also provides a script (scripts/create_lama_uhn.py) to create the data used in (Poerner et al., 2019).

Negated-LAMA

This repository also gives the option to evalute how pretrained language models handle negated probes (Kassner et al., 2019), set the flag use_negated_probes in scripts/run_experiments.py. Also, you should use this version of the LAMA probe https://dl.fbaipublicfiles.com/LAMA/negated_data.tar.gz

Reference:

The LAMA probe is described in the following paper:

@inproceedings{petroni2019language,
  title={Language Models as Knowledge Bases?},
  author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
  booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
  year={2019}
}

Other References

(Kassner et al., 2019) Nora Kassner, Hinrich Schütze. Negated LAMA: Birds cannot fly. arXiv preprint arXiv:1911.03343, 2019.
(Poerner et al., 2019) Nina Poerner, Ulli Waltinger, and Hinrich Schütze. BERT is Not a Knowledge Base (Yet): Factual Knowledge vs. Name-Based Reasoning in Unsupervised QA. arXiv preprint arXiv:1911.03681, 2019.
(Dai et al., 2019) Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G. Carbonell, Quoc V. Le, and Ruslan Salakhutdi. Transformer-xl: Attentive language models beyond a fixed-length context. CoRR, abs/1901.02860.
(Peters et al., 2018) Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. NAACL-HLT 2018
(Devlin et al., 2018) Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: pre-training of deep bidirectional transformers for language understanding. CoRR, abs/1810.04805.
(Radford et al., 2018) Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training.
(Liu et al., 2019) Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.

Licence

LAMA is licensed under the CC-BY-NC 4.0 license. The text of the license can be found here.

KuoHaoZeng / cse517-lama