gec-metrics 0.1.1

A library for Grammatical Error Correction evaluation.

gec-metrics

A library for evaluation of Grammatical Error Correction.

[API Docs] [Demo]

Install

pip install gec-metrics

To install latest version,

pip install git+https://github.com/gotutiyan/gec-metrics
python -m spacy download en_core_web_sm

Common Usage

API

Valid IDs for get_metric() can be found with get_metric_ids().

from gec_metrics import get_metric
metric_cls = get_metric('gleu')
metric = metric_cls(metric_cls.Config())
srcs = ['This sentences contain grammatical error .']
hyps = ['This sentence contains an grammatical error .']
refs = [
    ['This sentence contains an grammatical error .'],
    ['This sentence contains grammatical errors .']
] # (num_refs, num_sents)

# Corpus-level score
# If the metric is reference-free, the argument `references=` is not needed.
corpus_score: float = metric.score_corpus(
    sources=srcs,
    hypotheses=hyps,
    references=refs
)
# Sentence-level scores
sent_scores: list[float] = metric.score_sentence(
    sources=srcs,
    hypotheses=hyps,
    references=refs
)

CLI

• As the corresponding configurations differ depending on the metric, they are described and entered in yaml. If no yaml is provided, the default configuration is used.
• You can input multiple hypotheses.

gecmetrics-eval \
    --src <sources file> \
    --hyps <hypotheses file 1> <hypotheses file 2> ... \
    --refs <references file 1> <references file 2> ... \
    --metric <metric id> \
    --config config.yaml

# The output will be:
# Score=XXXXX | Metric=<metric id> | hyp_file=<hypotheses file 1>
# Score=XXXXX | Metric=<metric id> | hyp_file=<hypotheses file 2>
# ...

The config.yaml with default values can be generated via gecmetrics-gen-config.

gecmetrics-gen-config > config.yaml

Metrics

gec-metrics supports the following metrics.
All of arguments in the following examples indicate default values.

Reference-based

GLEU+ [Napoles+ 15] [Napoles+ 16]

from gec_metrics import get_metric
metric_cls = get_metric('gleu')
metric = metric_cls(metric_cls.Config(
    iter=500,  # The number of iterations 
    n=4,  # max n-gram
    unit='word'  # 'word' or 'char'
))

We also provide a reproduction of the official implementation as GLEUOfficial.
The official one ignores ngram frequency differences when calculating the difference set between source and reference.

from gec_metrics import get_metric
metric_cls = get_metric('gleuofficial')
metric = metric_cls(metric_cls.Config(
    iter=500,  # The number of iterations 
    n=4,  # max n-gram
    unit='word'  # 'word' or 'char'
))

ERRANT [Felice+ 16] [Bryant+ 17]

from gec_metrics import get_metric
metric_cls = get_metric('errant')
metric = metric_cls(metric_cls.Config(
    beta=0.5,  # The beta for F-beta score
    language='en'  # Language for SpaCy.
))

GoToScorer [Gotou+ 20]

from gec_metrics import get_metric
metric_cls = get_metric('gotoscorer')
metric = metric_cls(metric_cls.Config(
    beta=0.5,  # The beta for F-beta score
    ref_id=0,  # The reference id
    no_weight=False,  # If True, all weights are 1.0
    weight_file=''  # It is required if no_weight=False
))

You can generate a weight file via gecmetrics-gen-gotoscorer-weight.
The output is a JSON file.

gecmetrics-gen-gotoscorer-weight \
    --src <raw text file> \
    --ref <raw text file> \
    --hyp <raw text file 1> <raw text file 2> ... <raw text file N> \
    --out weight.json

PT-ERRANT [Gong+ 22]

from gec_metrics import get_metric
metric_cls = get_metric('pterrant')
weight_model_id = 'bertscore'
weight_model_cls = get_metric(weight_model_id)
metric = metric_cls(metric_cls.Config(
    beta=0.5,
    weight_model_name=weight_model_id,
    weight_model_config=weight_model_cls.Config(  # Optional: you can pass config
        score_type='f',
        rescale_with_baseline=True
    )
))

GREEN [Koyama+ 24]

from gec_metrics import get_metric
metric_cls = get_metric('green')
metric = metric_cls(metric_cls.Config(
    n=4,  # Max n of ngram
    beta=2.0,  # The beta for F-beta
    unit='word'  # 'word' or 'char'. Choose word-level or character-level
))

Reference-based (but sources-free)

These metrics are intended to be used for a component of PT-{M2, ERRANT}, but are also exposed to API.

BERTScore [Zhang+ 19]

The default config follows the default setting of [Gong+ 22].

from gec_metrics import get_metric
metric_cls = get_metric('bertscore')
metric = metric_cls(metric_cls.Config(
    model_type='bert-base-uncased',
    num_layers=None,
    batch_size=64,
    nthreads=4,
    all_layers=False,
    idf=False,
    idf_sents=None,
    lang='en',
    rescale_with_baseline=True,
    baseline_path=None,
    use_fast_tokenizer=False,
    score_type='f'
))

Reference-free

SOME [Yoshimura+ 20]

Download pre-trained models in advance from here.

from gec_metrics import get_metric
metric_cls = get_metric('some')
metric = metric_cls(metric_cls.Config(
    model_g='gfm-models/grammer',
    model_f='gfm-models/fluency',
    model_m='gfm-models/meaning',
    weight_f=0.55,
    weight_g=0.43,
    weight_m=0.02,
    batch_size=32
))

Scribendi [Islam+ 21]

from gec_metrics import get_metric
metric_cls = get_metric('scribendi')
metric = metric_cls(metric_cls.Config(
    model='gpt2',  # The model name or path to the language model to compute perplexity
    threshold=0.8  # The threshold for the maximum values of token-sort-ratio and levelshtein distance ratio
))

IMPARA [Maeda+ 22]

Note that the QE model is an unofficial model which achieves comparable correlation with the human evaluation results.
By default, it uses an unofficial pretrained QE model: [gotutiyan/IMPARA-QE].

from gec_metrics import get_metric
metric_cls = get_metric('impara')
metric = metric_cls(metric_cls.Config(
    model_qe='gotutiyan/IMPARA-QE',  # The model name or path for quality estimation.
    model_se='bert-base-cased',  # The model name or path for similarity estimation.
    threshold=0.9  # The threshold for the similarity score.
))

LLM-S, LLM-E [Kobayashi+24]

• llmkobayashi24 is a common prefix.
• llmkobayashi24hfsent and llmkobayashi24hfedit is a huggingface model based LLM-S and LLM-E.
• llmkobayashi24openaisent and llmkobayashi24openaiedit is a OpenAI model based LLM-S and LLM-E.

from gec_metrics import get_metric
metric_cls = get_metric('llmkobayashi24hfsent')
metric = metric_cls(metric_cls.Config(
    model='meta-llama/Llama-2-13b-chat-hf',  # The model name or path for a language model.
))

from gec_metrics import get_metric
metric_cls = get_metric('llmkobayashi24openaisent')
metric = metric_cls(metric_cls.Config(
    model='gpt-4o-mini-2024-07-18'
    organization='<Organization key>'
    api_key='<API key>'
    base_url=None,  # use it when using Gemini
))

Meta Evaluation

To perform meta evaluation easily, we provide meta-evaluation scripts.

Preparation

To donwload test data and human scores, you must download datasets by using the shell.

gecmetrics-prepare-meta-eval
# The above is the same as:
# bash src/gec_metrics/meta_eval/prepare_meta_eval.sh

This shell creates meta_eval_data/ directory which consists of SEEDA dataset and CoNLL14 official submissions.

meta_eval_data/
├── GJG15
│   └── judgments.xml
├── conll14
│   ├── official_submissions
│   │   ├── AMU
│   │   ├── CAMB
│   │   ├── ...
│   ├── REF0
│   └── REF1
└── SEEDA
    ├── outputs
    │   ├── all
    │   │   ├── ...
    │   └── subset
    │       ├── ...
    ├── scores
    │   ├── human
    │   │   ├── ...├── ...

Common Usage

gec_metrics.get_meta_eval() supports ['gjg', 'seeda'].

• .corr_system() performs system-level meta-evaluation.
• .corr_sentence() performs sentence-level meta-evaluation.

from gec_metrics import get_meta_eval
from gec_metrics import get_metric
metric_cls = get_metric('gleu')
metric = metric_cls(metric_cls.Config())
meta_cls = get_meta_eval('seeda')
meta = meta_cls(
    meta_cls.Config(system='base')
)
# System correlation
results = meta.corr_system(metric)
# Output:
# SEEDASystemCorrOutput(ew_edit=Corr(pearson=0.9007842791853424,
#                                    spearman=0.9300699300699302,
#                                    accuracy=None,
#                                    kendall=None),
#                       ew_sent=Corr(pearson=0.8749437873537543,
#                                    spearman=0.9090909090909092,
#                                    accuracy=None,
#                                    kendall=None),
#                       ts_edit=Corr(pearson=0.9123732084071973,
#                                    spearman=0.9440559440559443,
#                                    accuracy=None,
#                                    kendall=None),
#                       ts_sent=Corr(pearson=0.8856173179230024,
#                                    spearman=0.9020979020979022,
#                                    accuracy=None,
#                                    kendall=None))

# Sentence correlation
results = meta.corr_sentence(metric)
# Output:
# SEEDASentenceCorrOutput(sent=Corr(pearson=None,
#                                   spearman=None,
#                                   accuracy=0.6715701950751519,
#                                   kendall=0.3431403901503038),
#                         edit=Corr(pearson=None,
#                                   spearman=None,
#                                   accuracy=0.6734561494551116,
#                                   kendall=0.3469122989102231))

SEEDA: [Kobayashi+ 24]

from gec_metrics import get_meta_eval
meta_cls = get_meta_eval('seeda')
meta = meta_cls(
    meta_cls.Config(system='base')
)

The .corr_system() returns a gec_metrics.meta_eval.meta_eval.SEEDASystemCorrOutput. instance. This is a dataclass containig ew_sent, ew_edit, ts_sent, ts_edit.

• ew_* means using ExpectedWins human evaluation scores and ts_* means using TrueSkill.
• *_edit and *_sent means SEEDA-E and SEEDA-S.

The .corr_sentence() returns a gec_metrics.meta_eval.meta_eval.SEEDASentenceCorrOutput. instance. This is a dataclass containig sent, edit.

• edit and sent means SEEDA-E and SEEDA-S.

The window_analysis_system() performs the window analysis.

• This returns SEEDAWindowAnalysisSystemCorrOutput instance contaiing the same attributes as .corr_system(). Each attribute has dict[tuple, MetaEvalSEEDA.Corr] and the tuple means start and end rank of human evaluation.
• window_analysis_plot() can be used for visualization.

  # An exmaple of window-analysis visualization.
  from gec_metrics.metrics import ERRANT
  from gec_metrics.meta_eval import MetaEvalSEEDA
  import matplotlib.pyplot as plt
  metric = ERRANT(ERRANT.Config(beta=0.5))
  meta = MetaEvalSEEDA(
  MetaEvalSEEDA.Config(system='base')
  )
  window_results = meta.window_analysis_system(metric)
  fig = meta.window_analysis_plot(window_results.ts_edit)
  plt.savefig('window-errant.png')
  

GJG15: [Grundkiewicz+ 15]

This is referred to GJG15 in the SEEDA paper.
Basically, TrueSkill ranking is used to compute the correlation.

from gec_metrics import get_meta_eval
meta_cls = get_meta_eval('gjg')
meta = meta_cls()