ChemREL Command Line Interface (CLI).
Automate and transfer chemical data extraction using span categorization and relation extraction models.
To initialize the assets required by the CLI, run the following command.
$ chemrel initUsage:
$ chemrel [OPTIONS] COMMAND [ARGS]...Options:
--install-completion: Install completion for the current shell.--show-completion: Show completion for the current shell, to copy it or customize the installation.--help: Show this message and exit.
Commands:
aux: Run one of a number of auxiliary data...clean: Removes intermediate files to start data...init: Initializes files required by package at...predict: Predicts the spans and/or relations in a...rel: Configure and/or train a relation...span: Configure and/or train a span...
Run one of a number of auxiliary data processing commands.
Usage:
$ chemrel aux [OPTIONS] COMMAND [ARGS]...Options:
--help: Show this message and exit.
Commands:
extract-elsevier-paper: Converts Elsevier paper with specified DOI...extract-paper: Converts paper PDF at specified path into...
Converts Elsevier paper with specified DOI code into a sequence of JSONL files each corresponding to a text
chunk, where each JSONL line is tokenized by sentence. Example: if provided path is dir/file and the Paper text
contains two chunks, files dir/file_1.jsonl and dir/file_2.jsonl will be generated; otherwise, if the Paper
text contains one chunk, dir/file.jsonl will be generated.
Usage:
$ chemrel aux extract-elsevier-paper [OPTIONS] DOI_CODE API_KEY JSONL_PATHArguments:
DOI_CODE: DOI code of paper, not in URL form [required]API_KEY: Elsevier API key [required]JSONL_PATH: Filepath to save JSONL files to, ignores filename extension [required]
Options:
--char-limit INTEGER: Character limit of each text chunk in generated Paper object--help: Show this message and exit.
Converts paper PDF at specified path into a sequence of JSONL files each corresponding to a text chunk, where each
JSONL line is tokenized by sentence. Example: if provided path is dir/file and the Paper text contains two
chunks, files dir/file_1.jsonl and dir/file_2.jsonl will be generated; otherwise, if the Paper text contains
one chunk, dir/file.jsonl will be generated.
Usage:
$ chemrel aux extract-paper [OPTIONS] PAPER_PATH JSONL_PATHArguments:
PAPER_PATH: File path of paper PDF [required]JSONL_PATH: Filepath to save JSONL files to, ignores filename extension [required]
Options:
--char-limit INTEGER: Character limit of each text chunk in generated Paper object--help: Show this message and exit.
Removes intermediate files to start data preparation and training from a clean slate.
Usage:
$ chemrel clean [OPTIONS]Options:
--help: Show this message and exit.
Initializes files required by package at given path.
Usage:
$ chemrel init [OPTIONS] [PATH]Arguments:
[PATH]: File path in which to initialize required files [default: ./]
Options:
--help: Show this message and exit.
Predicts the spans and/or relations in a given text using the given models.
Usage:
$ chemrel predict [OPTIONS] COMMAND [ARGS]...Options:
--help: Show this message and exit.
Commands:
rel: Predicts spans and the relations between...span: Predicts spans contained in given text and...
Predicts spans and the relations between them contained in given text determined by the given models and prints them.
Usage:
$ chemrel predict rel [OPTIONS] SC_MODEL_PATH REL_MODEL_PATH TEXTArguments:
SC_MODEL_PATH: File path of span categorization model to be used [required]REL_MODEL_PATH: File path of relation extraction model to be used [required]TEXT: Text content to predict spans within [required]
Options:
--help: Show this message and exit.
Predicts spans contained in given text and prints them.
Usage:
$ chemrel predict span [OPTIONS] SC_MODEL_PATH TEXTArguments:
SC_MODEL_PATH: File path of span categorization model to be used [required]TEXT: Text content to predict spans within [required]
Options:
--help: Show this message and exit.
Configure and/or train a relation extraction model.
Usage:
$ chemrel rel [OPTIONS] COMMAND [ARGS]...Options:
--help: Show this message and exit.
Commands:
process-data: Parses the gold-standard annotations from...test: Applies the best relation extraction model...tl-cpu: Trains the relation extraction (rel) model...tl-gpu: Trains the relation extraction (rel) model...train-cpu: Trains the relation extraction (rel) model...train-gpu: Trains the relation extraction (rel) model...
Parses the gold-standard annotations from the Prodigy annotations.
Usage:
$ chemrel rel process-data [OPTIONS]Options:
--annotations-file TEXT: File path of Prodigy annotations [default: assets/goldrels.jsonl]--train-file TEXT: File path of training data corpus [default: reldata/train.spacy]--dev-file TEXT: File path of dev corpus [default: reldata/dev.spacy]--test-file TEXT: File path of test data corpus [default: reldata/test.spacy]--help: Show this message and exit.
Applies the best relation extraction model to unseen text and measures accuracy at different thresholds.
Usage:
$ chemrel rel test [OPTIONS]Options:
--trained-model TEXT: File path of trained model to be used [default: reltraining/model-best]--test-file TEXT: File path of test data corpus [default: reldata/test.spacy]--help: Show this message and exit.
Trains the relation extraction (rel) model using transfer learning on the CPU and evaluates it on the dev corpus.
Usage:
$ chemrel rel tl-cpu [OPTIONS]Options:
--tl-tok2vec-config TEXT: File path of config file for Tok2Vec span categorization model [default: configs/rel_TL_tok2vec.cfg]--train-file TEXT: File path of training data corpus [default: reldata/train.spacy]--dev-file TEXT: File path of dev corpus [default: reldata/dev.spacy]--help: Show this message and exit.
Trains the relation extraction (rel) model with a Transformer using transfer learning on the GPU and evaluates it on the dev corpus.
Usage:
$ chemrel rel tl-gpu [OPTIONS]Options:
--tl-trf-config TEXT: File path of config file for transformer span categorization model [default: configs/rel_TL_trf.cfg]--train-file TEXT: File path of training data corpus [default: reldata/train.spacy]--dev-file TEXT: File path of dev corpus [default: reldata/dev.spacy]--gpu-id TEXT: The GPU device identifier to be used [default: 0]--help: Show this message and exit.
Trains the relation extraction (rel) model on the CPU and evaluates it on the dev corpus.
Usage:
$ chemrel rel train-cpu [OPTIONS]Options:
--tok2vec-config TEXT: File path of config file for Tok2Vec span categorization model [default: configs/rel_tok2vec.cfg]--train-file TEXT: File path of training data corpus [default: reldata/train.spacy]--dev-file TEXT: File path of dev corpus [default: reldata/dev.spacy]--help: Show this message and exit.
Trains the relation extraction (rel) model with a Transformer on the GPU and evaluates it on the dev corpus.
Usage:
$ chemrel rel train-gpu [OPTIONS]Options:
--trf-config TEXT: File path of config file for transformer span categorization model [default: configs/rel_trf.cfg]--train-file TEXT: File path of training data corpus [default: reldata/train.spacy]--dev-file TEXT: File path of dev corpus [default: reldata/dev.spacy]--gpu-id TEXT: The GPU device identifier to be used [default: 0]--help: Show this message and exit.
Configure and/or train a span categorization model.
Usage:
$ chemrel span [OPTIONS] COMMAND [ARGS]...Options:
--help: Show this message and exit.
Commands:
process-data: Instructs to use the Prodigy function...test: Applies the best span categorization model...tl-cpu: Trains the span categorization (sc) model...tl-gpu: Trains the span categorization (sc) model...train-cpu: Trains the span categorization (sc) model...train-gpu: Trains the span categorization (sc) model...
Instructs to use the Prodigy function (data-to-spacy) for data processing.
Usage:
$ chemrel span process-data [OPTIONS]Options:
--help: Show this message and exit.
Applies the best span categorization model to unseen text and measures accuracy at different thresholds.
Usage:
$ chemrel span test [OPTIONS]Options:
--trained-model TEXT: File path of trained model to be used [default: sctraining/model-best]--test-file TEXT: File path of test data corpus [default: scdata/test.spacy]--gpu-id TEXT: The GPU device identifier to be used--help: Show this message and exit.
Trains the span categorization (sc) model using transfer learning on the CPU and evaluates it on the dev corpus.
Usage:
$ chemrel span tl-cpu [OPTIONS]Options:
--tl-tok2vec-config TEXT: File path of config file for Tok2Vec span categorization model [default: configs/sc_TL_tok2vec.cfg]--train-file TEXT: File path of training data corpus [default: scdata/train.spacy]--dev-file TEXT: File path of dev corpus [default: scdata/dev.spacy]--help: Show this message and exit.
Trains the span categorization (sc) model using transfer learning on the GPU and evaluates it on the dev corpus.
Usage:
$ chemrel span tl-gpu [OPTIONS]Options:
--tl-trf-config TEXT: File path of config file for transformer span categorization model [default: configs/sc_TL_trf.cfg]--train-file TEXT: File path of training data corpus [default: scdata/train.spacy]--dev-file TEXT: File path of dev corpus [default: scdata/dev.spacy]--gpu-id TEXT: The GPU device identifier to be used [default: 0]--help: Show this message and exit.
Trains the span categorization (sc) model on the CPU and evaluates it on the dev corpus.
Usage:
$ chemrel span train-cpu [OPTIONS]Options:
--tok2vec-config TEXT: File path of config file for Tok2Vec span categorization model [default: configs/sc_tok2vec.cfg]--train-file TEXT: File path of training data corpus [default: scdata/train.spacy]--dev-file TEXT: File path of dev corpus [default: scdata/dev.spacy]--help: Show this message and exit.
Trains the span categorization (sc) model on the GPU and evaluates it on the dev corpus.
Usage:
$ chemrel span train-gpu [OPTIONS]Options:
--trf-config TEXT: File path of config file for transformer span categorization model [default: configs/sc_trf.cfg]--train-file TEXT: File path of training data corpus [default: scdata/train.spacy]--dev-file TEXT: File path of dev corpus [default: scdata/dev.spacy]--gpu-id TEXT: The GPU device identifier to be used [default: 0]--help: Show this message and exit.