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Sequence labeling active learning framework for Python

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CI Status Poetry black pre-commit

PyPI Version Supported Python versions License

SeqAL is a sequence labeling active learning framework based on Flair.


Install this via pip (or your favourite package manager):

pip install seqal


Prepare data

The tagging scheme is the IOB scheme.

    U.N. NNP I-ORG
official NN  O
   Ekeus NNP I-PER
   heads VBZ O
     for IN  O
 Baghdad NNP I-LOC
       . .   O

Each line contains four fields: the word, its partof-speech tag and its named entity tag. Words tagged with O are outside of named entities.


Because SeqAL is based on flair, we heavily recommend to read the tutorial of flair first.

import json

from flair.embeddings import StackedEmbeddings, WordEmbeddings

from seqal.active_learner import ActiveLearner
from seqal.datasets import ColumnCorpus, ColumnDataset
from seqal.query_strategies import mnlp_sampling

# 1. get the corpus
columns = {0: "text", 1: "pos", 2: "ner"}
data_folder = "../conll"
corpus = ColumnCorpus(

First we need to create the corpus. date_folder is the directry path where we store datasets. contains NER labels, which usually just a small part of data (around 2% of total train data). and should contains NER labels for evaluation. All three kinds of data should follow the IOB scheme. But if you have 4 columns, you can just change columns to specify the tag column.

# 2. tagger params
tagger_params = {}
tagger_params["tag_type"] = "ner"  # what tag do we want to predict?
tagger_params["hidden_size"] = 256
embedding_types = [WordEmbeddings("glove")]
embeddings = StackedEmbeddings(embeddings=embedding_types)
tagger_params["embeddings"] = embeddings

# 3. Trainer params
trainer_params = {}
trainer_params["max_epochs"] = 10
trainer_params["mini_batch_size"] = 32
trainer_params["learning_rate"] = 0.01
trainer_params["train_with_dev"] = True

# 4. initialize learner
learner = ActiveLearner(tagger_params, mnlp_sampling, corpus, trainer_params)

This part is where we set the parameters for sequence tagger and trainer. The above setup can conver most of situations. If you want to add more paramters, I recommend to the read SequenceTagger and ModelTrainer in flair.

# 5. initial training"output/init_train")

The initial training will be trained on the seed data.

# 6. prepare data pool
pool_columns = {0: "text", 1: "pos"}
pool_file = data_folder + "/"
data_pool = ColumnDataset(pool_file, pool_columns)
sents = data_pool.sentences

Here we prepare the unlabeled data pool.

# 7. query data
query_number = 1
sents, query_samples = learner.query(sents, query_number, token_based=True)

We can query samples from data pool by the learner.query() method. query_number means how many sentence we want to query. But if we set token_based=True, the query_number means how many tokens we want to query. For the sequence labeling task, we usually set token_based=True.

query_samples is a list that contains queried sentences (the Sentence class in flair). sents contains the rest of unqueried sentences.

In [1]: query_samples[0].to_plain_string()
Out[1]: 'I love Berlin .'

We can get the text by calling to_plain_strin() method and put it into the interface for human annotation.

# 8. obtaining labels for "query_samples" by the human
query_labels = [
        "text": "I love Berlin .",
        "labels": [{"start_pos": 7, "text": "Berlin", "label": "S-LOC"}]
        "text": "This book is great.",
        "labels": []

annotated_sents = assign_labels(query_labels)

query_labels is the label information of a sentence after annotation by human. We use such information to create Flair Sentence class by calling assign_labels() method.

For more detail, see Adding labels to sentences

# 9. retrain model with new labeled data
learner.teach(annotated_sents, save_path=f"output/retrain")

Finally, we call learner.teach() to retrain the model. The annotated_sents will be added to corpus.train automatically.

If you want to run the workflow in a loop, you can take a look at the examples folders.

Construct envirement locally

If you want to make a PR or implement something locally, you can follow bellow instruction to construct the development envirement locally.

First we create a environment "seqal" based on the environment.yml file.

We use conda as envirement management tool, so install it first.

conda env create -f environment.yml

Then we activate the environment.

conda activate seqal

Install poetry for dependency management.

curl -sSL | python -

Add poetry path in your shell configure file (bashrc, zshrc, etc.)

export PATH="$HOME/.poetry/bin:$PATH"

Installing dependencies from pyproject.toml.

poetry install

You can make development locally now.

If you want to delete the local envirement, run below command.

conda remove --name seqal --all


See for detail.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

This project follows the all-contributors specification. Contributions of any kind welcome!


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