CLARIN Embeddings

State-of-the-art Text Representations for Natural Language Processing tasks, an initial version of library focus on the Polish Language

State-of-the-art Text Representations for Natural Language Processing tasks, an initial version of library focus on the Polish Language

This library was used during the development of the LEPISZCZE benchmark (NeurIPS 2022).

Installation

pip install clarinpl-embeddings

Example

Text-classification with polemo2 dataset and transformer-based embeddings

from embeddings.pipeline.lightning_classification import LightningClassificationPipeline

pipeline = LightningClassificationPipeline(
    dataset_name_or_path="clarin-pl/polemo2-official",
    embedding_name_or_path="allegro/herbert-base-cased",
    input_column_name="text",
    target_column_name="target",
    output_path="."
)

print(pipeline.run())

:warning: As for now, default pipeline model hyperparameters may provide poor results. It will be subject to change in further releases. We encourage users to use Optimized Pipelines to select appropriate hyperparameters.

Conventions

We use many of the HuggingFace concepts such as models (https://huggingface.co/models) or datasets (https://huggingface.co/datasets) to make our library as easy to use as it is possible. We want to enable users to create, customise, test, and execute NLP / NLU / SLU tasks in the fastest possible manner. Moreover, we present easy to use static embeddings, that were trained by CLARIN-PL.

Pipelines

We share predefined pipelines for common NLP tasks with corresponding scripts. For Transformer based pipelines we utilize PyTorch Lighting ⚡ trainers with Transformers AutoModels . For static embedding based pipelines we use Flair library under the hood.

Transformer embedding based pipelines (e.g. Bert, RoBERTA, Herbert):

Task Class Script
Text classification LightningClassificationPipeline evaluate_lightning_document_classification.py
Sequence labelling LightningSequenceLabelingPipeline evaluate_lightning_sequence_labeling.py

Run classification task

The example with non-default arguments

python evaluate_lightning_document_classification.py \
    --embedding-name-or-path allegro/herbert-base-cased \
    --dataset-name clarin-pl/polemo2-official \
    --input-columns-name text \
    --target-column-name target

Run sequence labeling task

The example with default language model and dataset.

python evaluate_lightning_sequence_labeling.py

Compatible datasets

As most datasets in HuggingFace repository should be compatible with our pipelines, there are several datasets that were tested by the authors.

dataset name task type input_column_name(s) target_column_name description
clarin-pl/kpwr-ner sequence labeling (named entity recognition) tokens ner KPWR-NER is a part of the Polish Corpus of Wrocław University of Technology (KPWr). Its objective is recognition of named entities, e.g., people, institutions etc.
clarin-pl/polemo2-official classification (sentiment analysis) text target A corpus of consumer reviews from 4 domains: medicine, hotels, products and school.
clarin-pl/2021-punctuation-restoration punctuation restoration text_in text_out Dataset contains original texts and ASR output. It is a part of PolEval 2021 Competition.
clarin-pl/nkjp-pos sequence labeling (part-of-speech tagging) tokens pos_tags NKJP-POS is a part of the National Corpus of Polish. Its objective is part-of-speech tagging, e.g., nouns, verbs, adjectives, adverbs, etc.
clarin-pl/aspectemo sequence labeling (sentiment classification) tokens labels AspectEmo Corpus is an extended version of a publicly available PolEmo 2.0 corpus of Polish customer reviews used in many projects on the use of different methods in sentiment analysis.
laugustyniak/political-advertising-pl sequence labeling (political advertising ) tokens tags First publicly open dataset for detecting specific text chunks and categories of political advertising in the Polish language.
laugustyniak/abusive-clauses-pl classification (abusive-clauses) text class Dataset with Polish abusive clauses examples.
allegro/klej-dyk pair classification (question answering)* (question, answer) target The Did You Know (pol. Czy wiesz?) dataset consists of human-annotated question-answer pairs.
allegro/klej-psc pair classification (text summarization)* (extract_text, summary_text) label The Polish Summaries Corpus contains news articles and their summaries.
allegro/klej-cdsc-e pair classification (textual entailment)* (sentence_A, sentence_B) entailment_judgment The polish sentence pairs which are human-annotated for textualentailment.


*only pair classification task is supported for now

Passing task model and task training parameters to predefined flair pipelines

Model and training parameters can be controlled via task_model_kwargs and task_train_kwargs parameters that can be populated using the advanced config. Tutorial on how to use configs can be found in /tutorials directory of the repository. Two types of config are defined in our library: BasicConfig and AdvancedConfig. In summary, the BasicConfig takes arguments and automatically assign them into proper keyword group, while the AdvancedConfig takes as the input keyword groups that should be already correctly mapped.

The list of available config can be found below:

Lightning:

  • LightningBasicConfig
  • LightningAdvancedConfig

Example with polemo2 dataset

Lightning pipeline

from embeddings.config.lightning_config import LightningBasicConfig
from embeddings.pipeline.lightning_classification import LightningClassificationPipeline

config = LightningBasicConfig(
    learning_rate=0.01, max_epochs=1, max_seq_length=128, finetune_last_n_layers=0,
    accelerator="cpu"
)

pipeline = LightningClassificationPipeline(
    embedding_name_or_path="allegro/herbert-base-cased",
    dataset_name_or_path="clarin-pl/polemo2-official",
    input_column_name=["text"],
    target_column_name="target",
    load_dataset_kwargs={
        "train_domains": ["hotels", "medicine"],
        "dev_domains": ["hotels", "medicine"],
        "test_domains": ["hotels", "medicine"],
        "text_cfg": "text",
    },
    output_path=".",
    config=config
)

You can also define an Advanced config with populated keyword arguments. In general, the keywords are passed to the object when constructing specific pipelines. We can identify and trace the keyword arguments to find the possible arguments that can be set in the config kwargs.

from embeddings.config.lightning_config import LightningAdvancedConfig

config = LightningAdvancedConfig(
    finetune_last_n_layers=0,
    task_train_kwargs={
        "max_epochs": 1,
        "devices": "auto",
        "accelerator": "cpu",
        "deterministic": True,
    },
    task_model_kwargs={
        "learning_rate": 5e-4,
        "use_scheduler": False,
        "optimizer": "AdamW",
        "adam_epsilon": 1e-8,
        "warmup_steps": 100,
        "weight_decay": 0.0,
    },
    datamodule_kwargs={
        "downsample_train": 0.01,
        "downsample_val": 0.01,
        "downsample_test": 0.05,
    },
    dataloader_kwargs={"num_workers": 0},
)

Available embedding models for Polish

Instead of the allegro/herbert-base-cased model, user can pass any model from HuggingFace Hub that is compatible with Transformers or with our library.

Embedding Type Description
clarin-pl/herbert-kgr10 bert HerBERT Large trained on supplementary data - the KGR10 corpus.
…

Optimized pipelines

Transformers embeddings

Task Optimized Pipeline
Lightning Text Classification OptimizedLightingClassificationPipeline
Lightning Sequence Labeling OptimizedLightingSequenceLabelingPipeline

Example with Text Classification

Optimized pipelines can be run via following snippet of code:


from embeddings.config.lighting_config_space import LightingTextClassificationConfigSpace
from embeddings.pipeline.lightning_hps_pipeline import OptimizedLightingClassificationPipeline

pipeline = OptimizedLightingClassificationPipeline(
    config_space=LightingTextClassificationConfigSpace(
        embedding_name_or_path="allegro/herbert-base-cased"
    ),
    dataset_name_or_path="clarin-pl/polemo2-official",
    input_column_name="text",
    target_column_name="target",
).persisting(best_params_path="best_prams.yaml", log_path="hps_log.pickle")
df, metadata = pipeline.run()

Training model with obtained parameters

After the parameters search process we can train model with best parameters found. But firstly we have to set output_path parameter, which is not automatically generated from OptimizedLightingClassificationPipeline.

metadata["output_path"] = "."

Now we are able to train the pipeline

from embeddings.pipeline.lightning_classification import LightningClassificationPipeline

pipeline = LightningClassificationPipeline(**metadata)
results = pipeline.run()

Selection of best embedding model

Instead of performing search with single embedding model we can search with multiple embedding models via passing them as list to ConfigSpace.

pipeline = OptimizedLightingClassificationPipeline(
    config_space=LightingTextClassificationConfigSpace(
        embedding_name_or_path=["allegro/herbert-base-cased", "clarin-pl/roberta-polish-kgr10"]
    ),
    dataset_name_or_path="clarin-pl/polemo2-official",
    input_column_name="text",
    target_column_name="target",
).persisting(best_params_path="best_prams.yaml", log_path="hps_log.pickle")

Citation

The paper describing the library is available on arXiv or in proceedings of NeurIPS 2022.

@inproceedings{augustyniak2022lepiszcze,
 author = {Augustyniak, Lukasz and Tagowski, Kamil and Sawczyn, Albert and Janiak, Denis and Bartusiak, Roman and Szymczak, Adrian and Janz, Arkadiusz and Szyma\'{n}ski, Piotr and W\k{a}troba, Marcin and Morzy, Miko\l aj and Kajdanowicz, Tomasz and Piasecki, Maciej},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
 pages = {21805--21818},
 publisher = {Curran Associates, Inc.},
 title = {This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/890b206ebb79e550f3988cb8db936f42-Paper-Datasets_and_Benchmarks.pdf},
 volume = {35},
 year = {2022}
}