0.3.0

New methods to transform a dataset:

• dataset.shuffle: create a shuffled dataset
• dataset.train_test_split: create a train and a test split (similar to sklearn)
• dataset.sort: create a dataset sorted according to a certain column
• dataset.select: create a dataset with rows selected following the given list of indices

Other features:

• Better instructions for datasets that require manual download

  Important: if you load datasets that require manual downloads with an older version of nlp, instructions won't be shown and an error will be raised

• Better access to dataset information (for instance dataset.feature['label'] or dataset.dataset_size)

Datasets:

• New: cos_e v1.0
• New: rotten_tomatoes
• New: german and italian wikipedia

New docs:

• documentation about splitting a dataset

Bug fixes:

• fix metric.compute that couldn't write on file
• fix squad_v2 imports

0.2.1

New datasets:

• ELI5
• CompGuessWhat?!
• BookCorpus
• Piaf
• Allociné
• BlendedSkillTalk

New features:

• .filter method
• option to do batching for metrics
• make datasets deterministic

New commands:

• nlp-cli upload_dataset
• nlp-cli upload_metric
• nlp-cli s3_datasets {ls,rm}
• nlp-cli s3_metrics {ls,rm}

New datasets + Apache Beam, new metrics, bug fixes

Datasets changes

• New: germeval14
• New: wmt
• New: Ubuntu dialog corpus
• New: squad spanish
• New: Quanta
• New: arcd
• New: Natural Questions (needs to be processed using a beam pipeline)
• New: C4 (needs to be processed using a beam pipeline)
• Skip the processing: wikipedia (english and french version are now already processed)
• Skip the processing: wiki40b (english version is now already processed)
• Renamed: anli -> art
• Better instructions: xsum
• Add .filter() for arrow datasets
• Add instruction message for manual data when required

Metrics changes:

• New: BERTScore
• Allow to add examples by element or by batch to compute a metric score

Commands:

• New: nlp-cli dummy_data: to help generate dummy data files to test dataset scripts
• New: nlp-cli run_beam: to run an apache beam pipeline to process a dataset in the cloud

Bug fixes:

• Now .map return the right values when run on different splits of the same dataset
• Fix input of the squad metric format to fit the format of the squad dataset
• Fix download from google drive for small files
• For datasets like glue or scientific paper, force the user to pick one sub-dataset to make things less confusing

More tests

• Local tests of dataset processing scripts
• AWS tests of dataset processing scripts
• Tests for arrow dataset methods
• Tests for arrow reader methods

First release

First release of the nlp library.

Read the README.md for an introduction: /~https://github.com/huggingface/nlp/blob/master/README.md

Tutorial: https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb

This is a beta release and the API is not expected to be stabilized yet (in particular the API for the metrics).

Documentation and tests are also still sparse.