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https://github.com/castorini/birch
Document ranking via sentence modeling using BERT
https://github.com/castorini/birch
Last synced: about 5 hours ago
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Document ranking via sentence modeling using BERT
- Host: GitHub
- URL: https://github.com/castorini/birch
- Owner: castorini
- Created: 2019-02-08T21:54:35.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T05:13:28.000Z (almost 2 years ago)
- Last Synced: 2024-08-02T13:28:07.040Z (3 months ago)
- Language: Python
- Homepage:
- Size: 852 KB
- Stars: 143
- Watchers: 11
- Forks: 30
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Birch
[ ](https://doi.org/10.5281/zenodo.3381673)
Document ranking via sentence modeling using BERT
Note:
The results in the arXiv paper [Simple Applications of BERT for Ad Hoc Document Retrieval](https://arxiv.org/abs/1903.10972) have been superseded by the results in the EMNLP'19 paper [Cross-Domain Modeling of Sentence-Level Evidence
for Document Retrieval].
To reproduce the results in the arXiv paper, please follow the instructions [here](https://github.com/castorini/birch/blob/master/reproduce_arxiv.md) instead.
## Environment & Data
```
# Set up environment
pip install virtualenv
virtualenv -p python3.5 birch_env
source birch_env/bin/activate
# Install dependencies
pip install Cython # jnius dependency
pip install -r requirements.txt
# For inference, the Python-only apex build can also be used
git clone https://github.com/NVIDIA/apex
cd apex && pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
# Set up Anserini (last reproduced with commit id: 5da46f610435be6364700bc5a6144253ed3f3b59)
git clone https://github.com/castorini/anserini.git
cd anserini && mvn clean package appassembler:assemble
cd eval && tar xvfz trec_eval.9.0.4.tar.gz && cd trec_eval.9.0.4 && make && cd ../../..
# Download data and models
wget https://zenodo.org/record/3381673/files/emnlp_bert4ir_v2.tar.gz
tar -xzvf emnlp_bert4ir_v2.tar.gz
```
Experiment Names:
- mb_robust04, mb_core17, mb_core18
- car_mb_robust04, car_mb_core17, car_mb_core18
- msmarco_mb_robust04, msmarco_mb_core17, msmarco_mb_core18
- robust04, car_core17, car_core18
- msmarco_robust04, msmarco_core17, msmarco_core18
## Training
For BERT(MB):
```
export CUDA_VISIBLE_DEVICES=0; experiment=mb; \
nohup python -u src/main.py --mode training --experiment ${experiment} --collection mb \
--local_model models/bert-large-uncased.tar.gz \
--local_tokenizer models/bert-large-uncased-vocab.txt --batch_size 16 \
--data_path data --predict_path data/predictions/predict.${experiment} \
--model_path models/saved.${experiment} --eval_steps 1000 \
--device cuda --output_path logs/out.${experiment} > logs/${experiment}.log 2>&1 &
```
For BERT(CAR -> MB) and BERT(MS MARCO -> MB):
```
export CUDA_VISIBLE_DEVICES=0; experiment=; \
nohup python -u src/main.py --mode training --experiment ${experiment} --collection mb \
--local_model \
--local_tokenizer models/bert-large-uncased-vocab.txt --batch_size 16 \
--data_path data --predict_path data/predictions/predict.${experiment} \
--model_path models/saved.${experiment} --eval_steps 1000 \
--device cuda --output_path logs/out.${experiment} > logs/${experiment}.log 2>&1 &
```
## Inference
For BERT(MB), BERT(CAR -> MB) and BERT(MS MARCO -> MB):
```
export CUDA_VISIBLE_DEVICES=0; experiment=; \
nohup python -u src/main.py --mode inference --experiment ${experiment} --collection \
--load_trained --model_path \
--batch_size 4 --data_path data --predict_path data/predictions/predict.${experiment} \
--device cuda --output_path logs/out.${experiment} > logs/${experiment}.log 2>&1 &
```
For BERT(CAR) and BERT(MS MARCO):
```
export CUDA_VISIBLE_DEVICES=0; experiment= \
--local_model \
--local_tokenizer models/bert-large-uncased-vocab.txt --batch_size 4 \
--data_path data --predict_path data/predictions/predict.${experiment} \
--device cuda --output_path logs/out.${experiment} > logs/${experiment}.log 2>&1 &
```
Note that this step takes a long time.
If you don't want to evaluate the pretrained models, you may skip to the next step and evaluate with our predictions under `data/predictions`.
## Retrieve sentences from top candidate documents
```
python src/utils/split_docs.py --collection \
--index --data_path data --anserini_path
```
## Evaluation
```
experiment=
collection=
anserini_path=
index_path=
data_path=
```
### BM25+RM3 Baseline
```
./eval_scripts/baseline.sh ${collection} ${index_path} ${anserini_path} ${data_path}
./eval_scripts/eval.sh baseline ${collection} ${anserini_path} ${data_path}
```
### Sentence Evidence
```
# Tune hyperparameters (if you do not have apex working, run this script with an additional "NOAPEX" param at the end)
./eval_scripts/train.sh ${experiment} ${collection} ${anserini_path}
# Run experiment
./eval_scripts/test.sh ${experiment} ${collection} ${anserini_path}
# Evaluate with trec_eval
./eval_scripts/eval.sh ${experiment} ${collection} ${anserini_path} ${data_path}
```