Query Setup

This tutorial shows how to make reasonable query.

from flair.embeddings import WordEmbeddings

from seqal.active_learner import ActiveLearner
from seqal.datasets import ColumnCorpus, ColumnDataset
from seqal.samplers import LeastConfidenceSampler
from seqal.utils import load_plain_text, add_tags

# 1~6 steps can be found in Introduction

# 7. query setup
query_number = 10
token_based = False
iterations = 5

# 8. iteration
for i in range(iterations):
    # 9. query unlabeled sentences
    queried_samples, unlabeled_sentences = learner.query(
        unlabeled_sentences, query_number, token_based=token_based, research_mode=False
    )

The query setup above is to query 10 sentences in each iterations. Usually we will make more 'smarter' query setup.

Query on Sentence

If we set token_based as False, this will count query_number as a sentence number. We prefer to give a percentage query number to query data instead of a fixed query number.

# 7. query setup
query_percent = 0.02
token_based = False
total_sentences = len(corpus.train.sentences) + len(data_pool.sentences)
query_number = int(total_sentences * query_percent)  # queried sentences in each iteration

# 8. iteration
for i in range(iterations):
    # 9. query unlabeled sentences
    queried_samples, unlabeled_sentences = learner.query(
        unlabeled_sentences, query_number, token_based=token_based, research_mode=False
    )

The query_percent could be 0.01 or 0.02.

Query on Token

If we set token_based as True, this will count query_number as a token number. In the real case, we usually query data based on the token. Because we don't know how many tokens a queried sentence has.

from seqal.utils import count_tokens

# 7. query setup
query_percent = 0.02
token_based = True
total_tokens = count_tokens(corpus.train.sentences) + count_tokens(data_pool.sentences)
query_number = tokens_each_iteration = int(total_tokens * query_percent)  # queried tokens in each iteration

# 8. iteration
for i in range(iterations):
    # 9. query unlabeled sentences
    queried_samples, unlabeled_sentences = learner.query(
        unlabeled_sentences, query_number, token_based=token_based, research_mode=False
    )