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LAMA: LAnguage Model Analysis

<img align="middle" src="img/logo.png" height="256" alt="LAMA">

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

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

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

Actually, LAMA is also a beautiful animal.

Reference:

The LAMA probe is described in the following papers:

@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}
}

@inproceedings{petroni2020how,
  title={How Context Affects Language Models' Factual Predictions},
  author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
  booktitle={Automated Knowledge Base Construction},
  year={2020},
  url={https://openreview.net/forum?id=025X0zPfn}
}

The LAMA probe

To reproduce our results:

1. Create conda environment and install requirements

(optional) It might be a good idea to 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

DISCLAIMER: ~55 GB on disk

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

python scripts/run_experiments.py

results will be logged in output/ and last_results.csv.

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

What else can you do with LAMA?

1. Encode a list of sentences

and use the vectors in your downstream task!

pip install -e git+https://github.com/facebookresearch/LAMA#egg=LAMA

import argparse
from lama.build_encoded_dataset import encode, load_encoded_dataset

PARAMETERS= {
        "lm": "bert",
        "bert_model_name": "bert-large-cased",
        "bert_model_dir":
        "pre-trained_language_models/bert/cased_L-24_H-1024_A-16",
        "bert_vocab_name": "vocab.txt",
        "batch_size": 32
        }

args = argparse.Namespace(**PARAMETERS)

sentences = [
        ["The cat is on the table ."],  # single-sentence instance
        ["The dog is sleeping on the sofa .", "He makes happy noises ."],  # two-sentence
        ]

encoded_dataset = encode(args, sentences)
print("Embedding shape: %s" % str(encoded_dataset[0].embedding.shape))
print("Tokens: %r" % encoded_dataset[0].tokens)

# save on disk the encoded dataset
encoded_dataset.save("test.pkl")

# load from disk the encoded dataset
new_encoded_dataset = load_encoded_dataset("test.pkl")
print("Embedding shape: %s" % str(new_encoded_dataset[0].embedding.shape))
print("Tokens: %r" % new_encoded_dataset[0].tokens)

2. Fill a sentence with a gap.

You should use the symbol [MASK] to specify the gap. Only single-token gap supported - i.e., a single [MASK].

python lama/eval_generation.py  \
--lm "bert"  \
--t "The cat is on the [MASK]."
<img align="middle" src="img/cat_on_the_phone.png" height="470" alt="cat_on_the_phone"> <img align="middle" src="img/cat_on_the_phone.jpg" height="190" alt="cat_on_the_phone"> <sub><sup>source: https://commons.wikimedia.org/wiki/File:Bluebell_on_the_phone.jpg</sup></sub>

Note that you could use this functionality to answer cloze-style questions, such as:

python lama/eval_generation.py  \
--lm "bert"  \
--t "The theory of relativity was developed by [MASK] ."

Install LAMA with pip

Clone the repo

git clone git@github.com:facebookresearch/LAMA.git && cd LAMA

Install as an editable package:

pip install --editable .

If you get an error in mac os x, please try running this instead

CFLAGS="-Wno-deprecated-declarations -std=c++11 -stdlib=libc++" pip install --editable .

Language Model(s) options

Option to indicate which language model(s) to use:

BERT

BERT pretrained models can be loaded both: (i) passing the name of the model and using huggingface cached versions or (ii) passing the folder containing the vocabulary and the PyTorch pretrained model (look at convert_tf_checkpoint_to_pytorch in here to convert the TensorFlow model to PyTorch).

RoBERTa

ELMo

Transformer-XL

GPT

Evaluate Language Model(s) Generation

options:

example considering both BERT and ELMo:

python lama/eval_generation.py \
--lm "bert,elmo" \
--bmd "pre-trained_language_models/bert/cased_L-24_H-1024_A-16/" \
--emd "pre-trained_language_models/elmo/original/" \
--t "The cat is on the [MASK]."

example considering only BERT with the default pre-trained model, in an interactive fashion:

python lamas/eval_generation.py  \
--lm "bert"  \
--i

Get Contextual Embeddings

python lama/get_contextual_embeddings.py \
--lm "bert,elmo" \
--bmn bert-base-cased \
--emd "pre-trained_language_models/elmo/original/"

Unified vocabulary

The intersection of the vocabularies for all considered models

Troubleshooting

If the module cannot be found, preface the python command with PYTHONPATH=.

If the experiments fail on GPU memory allocation, try reducing batch size.

Acknowledgements

Other References

Licence

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