This repository contains the codebase of our paper Annotating Columns with Pre-trained Language Models, available at arXiv and appearing at SIGMOD 2022.
$ git clone [link to repo]
$ cd doduo
$ pip install -r requirements.txt With conda, create a virtual environment and install the required packages as below:
$ conda create --name doduo python=3.7.10
$ conda activate doduo
$ pip install -r requirements.txtRun download.sh to download processed datasets for the VizNet corpus.
It will also create data directory.
$ bash download.sh
You will see the following files in data
$ ls data
msato_cv_0.csv
msato_cv_1.csv
msato_cv_2.csv
msato_cv_3.csv
msato_cv_4.csv
sato_cv_0.csv
sato_cv_1.csv
sato_cv_2.csv
sato_cv_3.csv
sato_cv_4.csv
table_col_type_serialized.pkl
table_rel_extraction_serialized.pklFor the WikiTable corpus, download the following files from here and save them under data/turl_dataset.
$ tree data/turl_dataset
data/turl_dataset
├── dev.table_col_type.json
├── dev.table_rel_extraction.json
├── test.table_col_type.json
├── test.table_rel_extraction.json
├── train.table_col_type.json
└── train.table_rel_extraction.jsonoptional arguments:
-h, --help show this help message and exit
--shortcut_name SHORTCUT_NAME
Huggingface model shortcut name
--max_length MAX_LENGTH
The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.
--batch_size BATCH_SIZE
Batch size
--epoch EPOCH Number of epochs for training
--random_seed RANDOM_SEED
Random seed
--num_classes NUM_CLASSES
Number of classes
--multi_gpu Use multiple GPU
--fp16 Use FP16
--warmup WARMUP Warmup ratio
--lr LR Learning rate
--tasks {sato0,sato1,sato2,sato3,sato4,msato0,msato1,msato2,msato3,msato4,turl,turl-re,turl-sch,turl-re-sch} [{sato0,sato1,sato2,sato3,sato4,msato0,msato1,msato2,msato3,msato4,turl,turl-re,turl-sch,turl-re-sch} ...]
Task name {sato, turl, turl-re, turl-sch, turl-re-sch}
--colpair Use column pair embedding
--train_ratios TRAIN_RATIOS [TRAIN_RATIOS ...]
e.g., --train_ratios turl=0.8 turl-re=0.1
--from_scratch Training from scratch
--single_col Training with single column modelTraining the full doduo model:
$ python doduo/train_multi.py --tasks turl turl_re-colpair --max_length 32 --batch_size 16
To specify GPU, use CUDA_VISIBLE_DEVICES environment variable. For example,
$ CUDA_VISIBLE_DEVICES=0 python doduo/train_multi.py --tasks turl --max_length 32 --batch_size 16After training, you will see the following files in the ./model directory.
$ ls model$ python doduo/predict_multi.py <model_path>$ python doduo/predict_multi.py model/turl_mosato_bert_bert-base-uncased-bs16-ml-32__turl-1.00Note while specifying model names, the last part of the model name "_best_macro_f1.pt" needs to be omitted.
The inference code will produce a JSON file in ./eval.
The Doduo Python module uses Pandas DataFrame as the base data structure. You can annotate any tables (i.e., only columns at this moment)
doduo = Doduo()
df = pd.read_csv("your_table.csv")
annotated_df = doduo.annotate_columns(df)Doduo.annotate_columns() will annotate the following three attributes to the input table and return an AnnotatedDataFrame object.
- coltypes (List[str])): Predicted column types
- colrels (List[str]): Predicted column relations
- colemb (List[np.ndarray]): Contextualized column embeddings
Let's take a look at examples.
import argparse
import pandas as pd
from doduo import Doduo
# Load Doduo model
args = argparse.Namespace
args.model = "wikitable" # or args.model = "viznet"
doduo = Doduo(args)
# Load sample tables
df1 = pd.read_csv("sample_tables/sample_table1.csv", index_col=0)
df2 = pd.read_csv("sample_tables/sample_table2.csv", index_col=0)
# Sample 1: Column annotation
annot_df1 = doduo.annotate_columns(df1)
print(annot_df1.coltypes)
# Sample 2: Column annotation
annot_df2 = doduo.annotate_columns(df2)
print(annot_df2.coltypes)@inbook{10.1145/3514221.3517906,
author = {Suhara, Yoshihiko and Li, Jinfeng and Li, Yuliang and Zhang, Dan and Demiralp, \c{C}a\u{g}atay and Chen, Chen and Tan, Wang-Chiew},
title = {Annotating Columns with Pre-trained Language Models},
year = {2022},
isbn = {9781450392495},
publisher = {Association for Computing Machinery},
url = {https://doi.org/10.1145/3514221.3517906},
booktitle = {Proceedings of the 2022 International Conference on Management of Data},
}
Datasets in this repo include variations of the "Sato" and "TURL" datasets.
Sato refers to the dataset used in "Sato: Contextual Semantic Type Detection in Tables." Proceedings of the VLDB Endowment Vol. 13, No.11. The dataset was generated from the VizNet corpus.
URL refers to the dataset used in "TURL: table understanding through representation learning." Proceedings of the VLDB Endowment 14.3 (2020): 307-319. The dataset was generated from the WikiTable corpus.
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All dataset and code used within the product are listed below (including their copyright holders and the license conditions). For Datasets having different portions released under different licenses, please refer to the included source link specified for each of the respective datasets for identifications of dataset files released under the identified licenses.
| ID | OSS Component Name | Modified | Copyright Holder | Upstream Link | License |
|---|---|---|---|---|---|
| 1 | BERT model | Yes | Hugging Face | link | Apache License 2.0 |
| ID | Dataset | Modified | Copyright Holder | Source Link | License |
|---|---|---|---|---|---|
| 1 | Sato dataset | No | source generated from VizNet | ||
| 2 | TURL dataset | No | source generated from WikiTable |