sota-extractor 0.0.29
Automatic SOTA (state-of-the-art) extraction.
License: Apache-2.0
Keywords: sota table

# Automatic SOTA (state-of-the-art) extraction

Aggregate public SOTA tables that are shared under **a free licences**. 

Download the scrapped data or run the scrappers yourself the get the latest data. 

In the future, we are planning to automate the process of extracting tasks, datasets and results from papers. 

## Getting the data

The data is kept in the [data](data/) directory. All data is shared under the [CC-BY-SA-4](https://creativecommons.org/licenses/by-sa/4.0/) licence. 
 
The data has been parsed into a consistent JSON format, described below. 

## JSON format description

The format consists of five primary data types: `Task`, `Dataset`, `Sota`, `SotaRow` and `Link`. 

A valid JSON file is a list of `Task` objects. You can see examples in the [data/tasks](https://github.com/atlasml/sota-extractor/tree/master/data/tasks) folder.

#### `Task`

A `Task` consists of the following fields:
- `task` - name of the task (string)
- `description` - short description of the task, in markdown (string)
- `subtasks` - a list of zero or more `Task` objects that are children to this task (list)
- `datasets` - a list of zero or more `Dataset` objects on which the tasks are evaluated (list)
- `source_link` - an optional `Link` object to the original source of the task

#### `Dataset`

A `Dataset` consists of the following fields:
- `dataset` - name of the dataset (string)
- `description` - a short description in markdown (string)
- `subdatasets` - zero or more children `Dataset` objects (e.g. dataset subsets or dataset partitions) (list)
- `dataset_links` - zero or more `Link` objects, representing the links to the dataset download page or any other relevant external pages (list)
- `dataset_citations"` - zero or more `Link` objects, representing the papers that are the primary citations for the dataset. 
- `sota` - the `Sota` object representing the state-of-the-art table on this dataset. 
 
#### `Link`

A `Link` object describes a URL, and has these two fields:
- `title` - title of the link, i.e. anchor text (string)
- `url` - target URL (string)

#### `Sota`

A `Sota` object represents one state-of-the-art table, with these fields:
- `metrics` - a list of metric names used to evaluate the methods (list of strings)
- `rows` a list of rows in the SOTA table, a list of `SotaRow` objects (list)

#### `SotaRow`

A `SotaRow` object represents one line of the SOTA table, it has these fields:
- `model_name` - Name of the model evaluated (string)
- `paper_title` - Primary paper's title (string)
- `paper_url` - Primary paper's URL (string)
- `paper_date` - Paper date of publishing, if available (string)
- `code_links` - a list of zero or more `Link` objects, with links to relevant code implementations (list)
- `model_links` - a list of zero or more `Link` objects, with links to relevant pretrained model files (list)
- `metrics` - a dictionary of values, where the keys are string from the parent `Sota.rows` list, and the values are the measured performance. (dictionary)

## Running the scrapers

### Installation

Requires Python 3.6+.

```bash
pip install -r requirements.txt
```

### NLP-progress

[NLP-progress](https://github.com/sebastianruder/NLP-progress) is a hand-annotated collection of SOTA results from NLP tasks. 

The scraper [is part of the NLP-progress project](https://github.com/sebastianruder/NLP-progress/pull/186).

Licence: MIT

### EFF 

EFF has annotated a set of SOTA results on a small number of tasks, and produced this [great report](https://www.eff.org/ai/metrics).

To convert the current content run:

```bash
python -m scrapers.eff
```

Licence: CC-BY-SA-4

### SQuAD

The [Stanford Question Answering Dataset](https://rajpurkar.github.io/SQuAD-explorer/) is an active project for evaluating the question answering task using a hidden test set. 

To scrape the current content run:

```bash
python -m scrapers.squad
```

Licence: CC-BY-SA-4

### RedditSota

The [RedditSota repository](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems) lists the best method for a variety of tasks across all of ML. 

To scrape the current content run:

```bash
python -m scrapers.redditsota
```

Licence: Apache-2

### SNLI

The [The Stanford Natural Language Inference (SNLI) Corpus](https://nlp.stanford.edu/projects/snli/) is an active project
for Natural Language Inference.  

To scrape the current content run:

```bash
python -m scrapers.snli
```

Licence: CC-BY-SA

### Cityscapes

[Cityscapes](https://www.cityscapes-dataset.com/benchmarks/#pixel-level-results) is a benchmark for semantic segmentation.

To scrape the current content run:

```bash
python -m scrapers.cityscapes
```

## Evaluating the SOTA extraction performance

In the future, this repository will also contain the automatic SOTA extraction pipeline. The aim is to automatically extract tasks, datasets and results from papers. 

To evaluate the current prediction performance for all tasks:

```bash
python -m extractor.eval_all
```

The most current report can be seen here: [eval_all_report.csv](data/eval_all_report.csv).