tabgenie 0.0.1

Description:

tabgenie 0.0.1

🧞 TabGenie: A Toolkit for Table-to-Text Generation
Demo 👉️ https://quest.ms.mff.cuni.cz/rel2text/tabgenie
TabGenie provides tools for working with data-to-text generation datasets in a unified tabular format.
TabGenie allows you to:

explore the content of the datasets
interact with table-to-text generation models
load and preprocess the datasets in a unified format
prepare spreadsheets for error analysis
export tables to various file formats

TabGenie is equipped with user-friendly web interface, Python bindings and command-line processing tools.
Frontend Preview

Quickstart
pip install tabgenie
tabgenie run --host=127.0.0.1 --port 8890
xdg-open http://127.0.0.1:8890

Or try the demo at:
👉️ https://quest.ms.mff.cuni.cz/rel2text/tabgenie
Datasets
The datasets are loaded from the HuggingFace datasets.
Input data in each dataset is preprocessed into a tabular format:

each table contains M rows and N columns,
cells may span multiple columns or rows,
cells may be marked as headings (indicated by bold font),
cells may be highlighted (indicated by yellow background).

Additionally, each example may contain metadata (such as title, url, etc.) which are displayed next to the main table as properties.



Dataset
Source
Data type
# train
# dev
# test
License




CACAPO
van der Lee et al. (2020)
Key-value
15,290
1,831
3,028
CC BY


DART
Nan et al. (2021)
Graph
62,659
2,768
5,097
MIT


E2E
Dušek et al. (2019)
Key-value
33,525
1,484
1,847
CC BY-SA


EventNarrative
Colas et al. (2021)
Graph
179,544
22,442
22,442
CC BY


HiTab
Cheng et al. (2021)
Table
7,417
1,671
1,584
C-UDA


Chart-to-text
Kantharaj et al. (2022)
Chart
24,368
5,221
5,222
GNU GPL


Logic2Text
Chen et al. (2020b)
Table + Logic
8,566
1,095
1,092
MIT


LogicNLG
Chen et al. (2020a)
Table
28,450
4,260
4,305
MIT


NumericNLG
Suadaa et al. (2021)
Table
1,084
136
135
CC BY-SA


SciGen
Moosavi et al. (2021)
Table
13,607
3,452
492
CC BY-NC-SA


SportSett:Basketball
Thomson et al. (2020)
Table
3,690
1,230
1,230
MIT


ToTTo
Parikh et al. (2020)
Table
121,153
7,700
7,700
CC BY-SA


WebNLG
Ferreira et al. (2020)
Graph
35,425
1,666
1,778
CC BY-NC


WikiBio
Lebret et al. (2016)
Key-value
582,659
72,831
72,831
CC BY-SA


WikiSQL
Zhong et al. (2017)
Table + SQL
56,355
8,421
15,878
BSD


WikiTableText
Bao et al. (2018)
Key-value
10,000
1,318
2,000
CC BY



See loaders/data.py for an up-to-date list of available datasets.
Requirements

Python 3
Flask
HuggingFace datasets

See setup.py for the full list of requirements.
Installation

pip: pip install tabgenie
development: pip install -e .[dev]
deployment: pip install -e .[deploy]

Web interface

local development: tabgenie [app parameters] run [--port=PORT] [--host=HOSTNAME]
deployment: gunicorn "tabgenie.cli:create_app([app parameters])"

Command-line Interface
Export data
Exports individual tables to file.
Usage:
tabgenie export \
--dataset DATASET_NAME \
--split SPLIT \
--out_dir OUT_DIR \
--export_format EXPORT_FORMAT

Supported formats: json, csv, xlsx, html, txt.
Generate a spreadsheet for error analysis
Generates a spreadsheet with system outputs and randomly selected examples for manual error analysis.
Usage:
tabgenie sheet \
--dataset DATASET \
--split SPLIT \
--in_file IN_FILE \
--out_file OUT_FILE \
--count EXAMPLE_COUNT

Show dataset details
Displays information about the dataset in YAML format (or the list of available datasets if no argument is provided).
Usage:
tabgenie info [-d DATASET]

Python
TabGenie can preprocess the datasets without dataset-specific preprocessing methods.
See the examples directory for a tutorial on using TabGenie for finetuning sequence-to-sequence models.
HuggingFace Integration
The datasets are stored to HF_DATASETS_CACHE directory which defaults to ~/.cache/huggingface/.
Set the HF_DATASETS_CACHE environment variable before launching tabgenie if you want to store the (potentially very large) datasets in a different directory.
The datasets are all loaded from HuggingFace datasets instead of their original repositories which allows to use preprocessed datasets and a single unified loader.
Note that there may be some minor changes in the data w.r.t. to the original datasets due to unification, such as adding "subject", "predicate" and "object" headings to RDF triple-to-text datasets.
Adding datasets
For adding a new dataset:

prepare the dataset

add the dataset to Huggingface Datasets
OR download the dataset locally


create the dataset loader in loaders

a subclass of HFTabularDataset for HF datasets
a subclass of TabularDataset for local datasets


create a mapping between the dataset name and the class name in loaders/__init__.py
add the dataset name to tabgenie/config.yml.

Each dataset should contain the prepare_table(entry) method which instantiates a Table object from the original entry.
The Table object is automatically exported to HTML and other formats (the methods may be overridden).
If a dataset is an instance of HFTabularDataset (i.e. is loaded from Huggingface Datasets), it should contain a self.hf_id attribute. The attribute is used to automatically load the dataset via datasets package.
Interactive mode
Pipelines are used for processing the tables and producing outputs.
See processing/processing.py for an up-to-date list of available pipelines.
Currently integrated:

model_api - a pipeline which generates a textual description of a table by calling a table-to-text generation model through API,
graph - a pipeline which creates a knowledge graph by extracting RDF triples from a table and visualizes the output using D3.js library,

Adding pipelines
For adding a new pipeline:

create a file in processing/pipelines containing the pipeline class,
create file(s) in processing/processors with processors needed for the pipeline,
add the mapping between pipeline name and class name to get_pipeline_class_by_name() in processing/processing.py.

Each pipeline should define self.processors in the __init__() method, instantiating the processors needed for the pipeline.
The input to each pipeline is a content object containing several fields needed for table processing. This interface may be subject to change (see __init.py_:run_pipeline() for more details).
The processors serve as modules, i.e. existing processors can be combined to create new pipelines. The interface between the processors may vary, it is however expected that the last processor in the pipeline outputs HTML code which is displayed on the page.
Pipeline config
This is an example pipeline configuration in tabgenie/config.yml:
rdf_triples:
pipeline: graph
interactive: true
datasets:
- webnlg
- dart
- e2e

The key rdf_triples is the name of the pipeline which will be displayed in the web interface. It should contain only letters of English alphabet, underscores _ or dashes -.
Required arguments:

pipeline : str - the name of the pipeline as defined in processing/processing.py, will be mapped to pipeline class
interactive: bool - whether the pipeline will be displayed in the interactive mode in the web interface

Optional arguments:

datasets : list - the list of datasets for which the pipeline will be active in the web interface (all datasets by default)
any other argument, will be passed to the pipeline in pipeline_args

Configuration
The global configuration is stored in the tabgenie/config.yml file.

datasets - datasets which will be available in the web interface,
default_dataset - the dataset which is loaded by default,
host_prefix - subdirectory on which the app is deployed (used for loading static files and sending POST requests),
cache_dev_splits - whether to preload all available dev sets after startup,
generated_outputs_dir - directory from which the generated outputs are loaded,
pipelines - pipelines which will be available in the web interface (see the Interactive mode section for more info).