Read the Paper: https://arxiv.org/abs/1907.09190
Read the Blog Post: https://ai.facebook.com/blog/longform-qa/
Explore the Dataset: https://facebookresearch.github.io/ELI5/explore.html
We provide a suite of scripts to download paired questions and answers from the ELI5 subreddit along with supporting documents from the CommonCrawl
No, we are not allowed to host processed Reddit or CommonCrawl data. While we are aware that it would make life a little easier, making your own version of the dataset following the instructions here is pretty straightforward if you have access to a SLURM cluster. We're also happy to work with you if your cluster works on another operating system, open an issue and we'll get on it! Since the creation process involves downloading, filtering and tokenizing a full CommonCrawl dump however, using a single machine is impractical.
The process consists of three steps. Steps 1 and 2 should be run in parallel.
- Downloading and filtering the Reddit data. This can be run on a single machine and may take up to 72 hours.
- Downloading and tokenizing the CommonCrawl pages. This part requires access to a cluster. We provide a sample SLURM script using 100 threads, which on our cluster finishes in under 48 hours.
- Selecting passages from the downloaded pages to create the final support document. After running steps 1 and 2, this part uses our TFIDF heuristic to create the final ~1000 words support document, and create a train/valid/test split of Question-Document-Answer triplets.
If you are having trouble with any of these, please open an issue stating which step is failing in the title, and attach the Python error text, if available.
The first step consists in downloading the Reddit Data from the files provided at pushshift.io for all months from 07/2011 to 07/2018. This is done by running:
python download_reddit_qalist.py -Q
python download_reddit_qalist.py -A
The first line takes about 6 hours on one machine to download the questions, and the second less than 48 hours for the answers. Pushshift files are automatically removed after they've been processed, so space shouldn't be an issue there. The final product should be 689MB.
We provide a list of CommonCrawl IDs for supporting documents for each of the questions. This can be obtained at:
cd pre_computed
wget https://commoncrawl.s3.amazonaws.com/crawl-data/CC-MAIN-2018-34/wet.paths.gz
gunzip wet.paths.gz
wget https://dl.fbaipublicfiles.com/eli5qa/explainlikeimfive_ccrawl_ids.json.gz
gunzip explainlikeimfive_ccrawl_ids.json.gz
wget https://dl.fbaipublicfiles.com/eli5qa/explainlikeimfive_unigram_counts.json
cd ..
The next step than consists in reading through the CommonCrawl WET files to gather the text of pages which are used as support documents. In order to gather the documents for each QA pair (up to 100 per pair, sometimes less after deduplicating) using a SLURM cluster and 100 threads, run:
cd slurm_scripts
./eli_download_docs_launcher.sh
This should run in less than 48 hours. Be advised that the result is upwards of 100GB.
When you have downloaded the selected pages from all of the CommonCrawl slices, simply merge the slices from all the threads with:
./eli_merge_docs_launcher.sh
cd ..
python merge_support_docs.py explainlikeimfive finalize
FAQ: my SLURM cluster is not very stable and some of the threads are interrupted. Do I need to re-run everything?
To check whether all slices are finished, simply look at the collected_docs/tmp/counts_*.json files. All the files corresponding to completed slices should only have the string finished, so for example:
ELI5/data_creation/processed_data$ grep finished collected_docs/tmp/counts_* | wc
If the number of finished thread is less than 100, simply relaunch the missing ones, the code will restart them from their last saved space. To launch a single thread (e.g. 34), go to the data_creation folder and run:
python download_support_docs.py -ns 34
All that remains to do now is to map the collected passages to the question-answer pairs and, apply our provided heuristic to make a single support document to select relevant passages
cd slurm_scripts
./eli_select_docs_launcher.sh
And finally, make the train, valid and test split with:
cd ..
python finalize_qda.py
rm processed_data/selected_15_1/explainlikeimfive/selected_slice_*.json
Congrats, you can now start working on your very own Long-Form Question Answering systems!
ELI5/data_creation$ ls -lh processed_data/selected_15_1/explainlikeimfive*.json
-rw-rw-r-- 1 username username 142M Oct 15 06:18 processed_data/selected_15_1/explainlikeimfive_test.json
-rw-rw-r-- 1 username username 1.3G Oct 15 06:18 processed_data/selected_15_1/explainlikeimfive_train.json
-rw-rw-r-- 1 username username 57M Oct 15 06:18 processed_data/selected_15_1/explainlikeimfive_valid.json
We provide a script to convert the json formatted files to .txt files for source and target and build the multi-tasking source-target pairs. Modify the input and output path accordingly.
cd model_code
OUTPUT_PATH=formatted_files
PATH_TO_DATA=processed_data/selected_15_1
mkdir $OUTPUT_PATH
python process_data_to_source_target.py --input $PATH_TO_DATA --output $OUTPUT_PATH
We use bpe and release the BPE codes we used. You will need to apply BPE on the data. We provide a sample command:
subword-nmt apply-bpe -c model_code/bpe_codes.txt < formatted_files/train.qd_source > formatted_files/train.qd_source_bpe
subword-nmt apply-bpe -c model_code/bpe_codes.txt < formatted_files/test.qd_source > formatted_files/test.qd_source_bpe
subword-nmt apply-bpe -c model_code/bpe_codes.txt < formatted_files/valid.qd_source > formatted_files/valid.qd_source_bpe
subword-nmt apply-bpe -c model_code/bpe_codes.txt < formatted_files/train.qd_target > formatted_files/train.qd_target_bpe
subword-nmt apply-bpe -c model_code/bpe_codes.txt < formatted_files/test.qd_target > formatted_files/test.qd_target_bpe
subword-nmt apply-bpe -c model_code/bpe_codes.txt < formatted_files/valid.qd_target > formatted_files/valid.qd_target_bpe
We use the Fairseq-py sequence-to-sequence library. For details about this library, please see the main repository or read the full documentation. We provide example commands below. Please modify the paths accordingly to where you have stored the data, where you would like the binarized data to be located, and where the trained model checkpoints have been stored.
To binarize the data:
cd fairseq
TEXT=formatted_files
python preprocess.py --source-lang qd_source_bpe --target-lang qd_target_bpe \
--validpref $TEXT/valid --testpref $TEXT/test --trainpref $TEXT/train --destdir data-bin/eli5
If you would like to binarize the dataset with our dictionary instead of creating your own (for example, if you would like to load our pretrained model), you can add the following parameters to the above command:
--srcdict model_code/dict.multitask_source_bpe.txt --tgtdict model_code/dict.multitask_target_bpe.txt
To train the model:
cd fairseq
python train.py data-bin/eli5 --task translation --source-lang qd_source_bpe --target-lang qd_target_bpe --arch transformer_wmt_en_de_big_t2t --share-decoder-input-output-embed --dropout 1e-1 --attention-dropout 1e-1 --relu-dropout 1e-1 --criterion label_smoothed_cross_entropy --label-smoothing 1e-1 --optimizer adam --adam-betas '(0.9, 0.98)' --lr 1e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 --warmup-init-lr 1e-7 --min-lr 1e-9 --clip-norm 0 --no-progress-bar --log-interval 100
Depending on the model you might need to increase the values for --max-source-positions and --max-target-positions (e.g., to 4096), and set --max-tokens and --update-freq to a suitable value.
To generate from the model:
cd fairseq
PATH_TO_CHECKPOINT=model_checkpoint.pt
python generate.py data-bin/eli5 --path $PATH_TO_CHECKPOINT --gen-subset valid --task translation --nbest 1 --source-lang qd_source_bpe --target-lang qd_target_bpe --beam 5 --batch-size 32 --remove-bpe --no-repeat-ngram-size 3 --max-len-b 500 --min-len 200 --max-source-positions 4096 --max-target-positions 4096 --skip-invalid-size-inputs-valid-test --model-overrides "{'max_source_positions':4096, 'max_target_positions':4096}"
to evaluate on the test set, set:
--gen-subset test
We provide a script used to compute ROUGE. Given a file of model generated hypotheses and a file of true references, it can be run in the following manner:
pip install rouge
HYPOTHESES=model_hypotheses.txt
REFERENCES=true_references.txt
python compute_rouge.py --hypotheses $HYPOTHESES --references $REFERENCES
The min and max length of generation were tuned. For partial fill ROUGE, we evaluated fixed length generation (as model generated answers are usually tuned to be a lot longer than human written answers) based on the validation set.
We provide a pretrained model, which you can download here:
wget https://dl.fbaipublicfiles.com/eli5qa/multitask_checkpoint.pt
To use the pretrained model, you will need to follow these steps:
First, the multi-task model requires labeling the source with the task label (e.g. question answering v. language modeling). We provide a script here to do this:
python model_code/prepare_multitask_input.py --input $DATA_FILE --output $OUTPUT_DATA_FILE
Then, apply the BPE:
subword-nmt apply-bpe -c model_code/bpe_codes.txt < $OUTPUT_DATA_FILE > $OUTPUT_DATA_FILE_BPE
Now, you are ready to forward the model on your BPE'd data. You can generate from the model using Fairseq-py generate.py or interactive.py commands.
Check out the file test_model_code_scripts.sh which runs all of the model scripts we include. The sample input/output of these scripts is included in the folder testing_files for your reference. If you are having trouble, please take a look at these sample files we used for testing to make sure you have the correct input format.
Please cite as:
@inproceedings{fan2019eli5,
title = {ELI5: Long Form Question Answering},
author = {Angela Fan and Yacine Jernite and Ethan Perez and David Grangier and Jason Weston and Michael Auli},
booktitle = {Proceedings of ACL 2019},
year = {2019},
}