bratevalwrapper4nlp 0.0.2

A small wrapper for BratEval that encapsulates the java commands

BratEval Wrapper for NLP

This library wraps the Java-based BratEval utility to evaluate annotation data for named-entity-recognition (NER). Given the availability of the Git, Java JDK and Maven, it clones and compiles brateval and wraps the io interactions into Python.

Note that currently the release v0.3.2 of brateval is used. See: https://github.com/READ-BioMed/brateval/tree/v0.3.2

Note that a valid Java JDK and Maven environment must be set up correctly.

Install

First, make sure to install a Java JDK environment as well as Maven (for compiling the JAR file) before using the wrapper.

[Click to show] Preparation instructions for Ubuntu

# Install Java (11, or 21), Maven and Git first
sudo apt install -y openjdk-21-jdk-headless maven git

# Add JAVA_HOME variable to ~/.bashrc
echo 'export JAVA_HOME=$(readlink -f /usr/bin/javac | sed "s:/bin/javac::")' >> $HOME/.bashrc

# Login again to re-load the JAVA_HOME environment variable (or export the variable manually)
export JAVA_HOME=$(readlink -f /usr/bin/javac | sed "s:/bin/javac::")

Eventually, install the package using pip: python3 -m pip install bratevalwrapper4nlp

Example

The following script demonstrates the use of the evaluate function.

import json
from bratevalwrapper4nlp import evaluate

# Define document (ground truth and prediction)
doc_ground_truth = {
    "text": "This is a fine example.",
    "label": [
        (10, 14, "LABEL2"),
        (15, 22, "LABEL1"),
    ]
}
doc_prediction = {
    "text": "This is a fine example.",
    "label": [
        (10, 22, "LABEL1"),
    ]
}

# Verify the text spans
for src, doc in {"Ground Truth": doc_ground_truth, "Prediction": doc_prediction}.items():
    for lbl_start, lbl_stop, lbl_cls in doc.get("label", []):
        print("[{}] {} has label {}".format(
            src,
            repr(doc["text"][lbl_start:lbl_stop]),
            lbl_cls
        ))

# Run evaluation
score_response = evaluate(
    doc_ground_truth,     # or list of docs: [doc_ground_truth]
    doc_prediction,       # or list of docs: [doc_prediction]
    span_match="overlap", # "overlap", "exact", or float (overlap percentage between 1.0 and 0.0)
    type_match="exact"    # "exact", or "inexact" (ignores label classes)
)
scores = score_response["scores"]

print("Obtained scores:")
print(json.dumps(scores, indent=2))