This library includes processors for several traditional tasks. These processors can be used to process a dataset into examples that can be fed to a model.
All processors follow the same architecture which is that of the
DataProcessor. The processor returns a list
InputExample can be converted to
InputFeatures in order to be fed to the model.
General Language Understanding Evaluation (GLUE) is a benchmark that evaluates the performance of models across a diverse set of existing NLU tasks. It was released together with the paper GLUE: A multi-task benchmark and analysis platform for natural language understanding
This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched), CoLA, SST2, STSB, QQP, QNLI, RTE and WNLI.
- Those processors are:
Additionally, the following method can be used to load values from a data file and convert them to a list of
The Cross-Lingual NLI Corpus (XNLI) is a benchmark that evaluates the quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on MultiNLI <http://www.nyu.edu/projects/bowman/multinli/>: pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-ressource language such as English and low-ressource languages such as Swahili).
It was released together with the paper XNLI: Evaluating Cross-lingual Sentence Representations
- This library hosts the processor to load the XNLI data:
Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
An example using these processors is given in the run_xnli.py script.
The Stanford Question Answering Dataset (SQuAD) is a benchmark that evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version (v1.1) was released together with the paper SQuAD: 100,000+ Questions for Machine Comprehension of Text. The second version (v2.0) was released alongside the paper Know What You Don’t Know: Unanswerable Questions for SQuAD.
This library hosts a processor for each of the two versions:
- Those processors are:
They both inherit from the abstract class
Additionally, the following method can be used to convert SQuAD examples into
that can be used as model inputs.
These processors as well as the aforementionned method can be used with files containing the data as well as with the tensorflow_datasets package. Examples are given below.
Here is an example using the processors as well as the conversion method using data files:
# Loading a V2 processor processor = SquadV2Processor() examples = processor.get_dev_examples(squad_v2_data_dir) # Loading a V1 processor processor = SquadV1Processor() examples = processor.get_dev_examples(squad_v1_data_dir) features = squad_convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=max_seq_length, doc_stride=args.doc_stride, max_query_length=max_query_length, is_training=not evaluate, )
Using tensorflow_datasets is as easy as using a data file:
# tensorflow_datasets only handle Squad V1. tfds_examples = tfds.load("squad") examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate) features = squad_convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=max_seq_length, doc_stride=args.doc_stride, max_query_length=max_query_length, is_training=not evaluate, )
Another example using these processors is given in the run_squad.py script.