flair.datasets.biomedical.CEMP#
- class flair.datasets.biomedical.CEMP(base_path=None, in_memory=True, sentence_splitter=None)View on GitHub#
Bases:
ColumnCorpus
Original CEMP corpus containing chemical annotations.
For further information see: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-v/cemp-detailed-task-description/
- __init__(base_path=None, in_memory=True, sentence_splitter=None)View on GitHub#
Initialize the CEMP corpus.
- Parameters:
base_path (
Union
[str
,Path
,None
]) – Path to the corpus on your machinein_memory (
bool
) – If True, keeps dataset in memory giving speedups in training.sentence_splitter (
Optional
[SentenceSplitter
]) – Implementation ofSentenceSplitter
which segments documents into sentences and tokens (defaultSciSpacySentenceSplitter
)
Methods
__init__
([base_path, in_memory, ...])Initialize the CEMP corpus.
add_label_noise
(label_type, labels[, ...])Generates uniform label noise distribution in the chosen dataset split.
download_dev_corpus
(data_dir)download_train_corpus
(data_dir)downsample
([percentage, downsample_train, ...])Randomly downsample the corpus to the given percentage (by removing data points).
filter_empty_sentences
()A method that filters all sentences consisting of 0 tokens.
filter_long_sentences
(max_charlength)A method that filters all sentences for which the plain text is longer than a specified number of characters.
get_all_sentences
()Returns all sentences (spanning all three splits) in the
Corpus
.get_label_distribution
()Counts occurrences of each label in the corpus and returns them as a dictionary object.
make_label_dictionary
(label_type[, ...])Creates a dictionary of all labels assigned to the sentences in the corpus.
make_tag_dictionary
(tag_type)Create a tag dictionary of a given label type.
make_vocab_dictionary
([max_tokens, min_freq])Creates a
Dictionary
of all tokens contained in the corpus.obtain_statistics
([label_type, pretty_print])Print statistics about the corpus, including the length of the sentences and the labels in the corpus.
parse_input_file
(text_file, ann_file)Attributes
dev
The dev split as a
torch.utils.data.Dataset
object.test
The test split as a
torch.utils.data.Dataset
object.train
The training split as a
torch.utils.data.Dataset
object.- classmethod download_train_corpus(data_dir)View on GitHub#
- Return type:
Path
- classmethod download_dev_corpus(data_dir)View on GitHub#
- Return type:
Path
- static parse_input_file(text_file, ann_file)View on GitHub#
- Return type: