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    "info": {
        "author": "Hironsan",
        "author_email": "hiroki.nakayama.py@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
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        "description": "# seqeval\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script [conlleval](https://www.clips.uantwerpen.be/conll2002/ner/bin/conlleval.txt),\nwhich can be used for measuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\n## Support features\nseqeval supports following formats:\n* IOB1\n* IOB2\n* IOE1\n* IOE2\n* IOBES\n\nand supports following metrics:\n\n| metrics  | description  |\n|---|---|\n| accuracy_score(y\\_true, y\\_pred)  | Compute the accuracy.  |\n| precision_score(y\\_true, y\\_pred)  | Compute the precision.  |\n| recall_score(y\\_true, y\\_pred)  | Compute the recall.  |\n| f1_score(y\\_true, y\\_pred)  | Compute the F1 score, also known as balanced F-score or F-measure.  |\n| classification_report(y\\_true, y\\_pred, digits=2)  | Build a text report showing the main classification metrics. `digits` is number of digits for formatting output floating point values. Default value is `2`. |\n\n## Usage\nBehold, the power of seqeval:\n\n```python\n>>> from seqeval.metrics import accuracy_score\n>>> from seqeval.metrics import classification_report\n>>> from seqeval.metrics import f1_score\n>>> \n>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n>>>\n>>> f1_score(y_true, y_pred)\n0.50\n>>> accuracy_score(y_true, y_pred)\n0.80\n>>> classification_report(y_true, y_pred)\n             precision    recall  f1-score   support\n\n       MISC       0.00      0.00      0.00         1\n        PER       1.00      1.00      1.00         1\n\n  micro avg       0.50      0.50      0.50         2\n  macro avg       0.50      0.50      0.50         2\n```\n\n### Keras Callback\n\nSeqeval provides a callback for Keras:\n\n```python\nfrom seqeval.callbacks import F1Metrics\n\nid2label = {0: '<PAD>', 1: 'B-LOC', 2: 'I-LOC'}\ncallbacks = [F1Metrics(id2label)]\nmodel.fit(x, y, validation_data=(x_val, y_val), callbacks=callbacks)\n```\n\n## Installation\nTo install seqeval, simply run:\n\n```\n$ pip install seqeval[cpu]\n```\n\nIf you want to install seqeval on GPU environment, please run:\n\n```bash\n$ pip install seqeval[gpu]\n```\n\n## Requirement\n\n* numpy >= 1.14.0\n* tensorflow(optional)",
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