Evaluation as a Service for Natural Language Processing
Project description
EaaS_API
Documentation
Documentation at https://expressai.github.io/autoeval/. Some references for writing docs can refer to
- https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html#rst-primer
- https://sphinx-tutorial.readthedocs.io/step-1/
- https://sphinx-themes.org/sample-sites/furo/
Usage
To install the API, simply run
pip install eaas
To use the API, You should go through the following two steps.
- Step 1: You should load the default configurations and make modifications based on your own needs.
from eaas import Config
config = Config()
# To see the metrics we support, run
print(config.metrics())
# dict_keys(['bart_score_summ', 'bart_score_mt', 'bert_score', 'bleu', 'chrf', 'comet', 'comet_qe', 'mover_score', 'prism', 'prism_qe', 'rouge1', 'rouge2', 'rougeL'])
# To see the default configuration of a metric, run
print(config.bleu.to_dict())
# {'smooth_method': 'exp', 'smooth_value': None, 'force': False, 'lowercase': False, 'use_effective_order': False}
# To modify the config, run
config.bleu.set_property("smooth_method", "floor")
print(config.bleu.to_dict())
# {'smooth_method': 'floor', 'smooth_value': None, 'force': False, 'lowercase': False, 'use_effective_order': False}
- Step 2: Initialize the client and send your inputs.
from eaas import Client
client = Client()
client.load_config(config) # The config you have created above
# To use this API for scoring, you need to format your input as list of dictionary.
# Each dictionary consists of `source` (string, optional), `references` (list of string, optional)
# and `hypothesis` (string, required). `source` and `references` are optional based on the metrics
# you want to use. Please do not conduct any preprocessing on `source`, `references` or `hypothesis`,
# we expect normal-cased detokenized texts. All the preprocessing steps are taken by the metrics.
# Below is a simple example.
inputs = [{"source": "This is the source.",
"references": ["This is the reference one.", "This is the reference two."],
"hypothesis": "This is the generated hypothesis."}]
metrics = ["bleu", "chrf"] # Can be None for simplicity if you consider using all metrics
score_dic = client.score(inputs, task="sum", metrics=metrics, lang="en")
# inputs is a list of Dict, task is the name of task, metrics is metric list, lang is the two-letter code language
The output is like
# sample_level is a list of dict, corpus_level is a dict
{
'sample_level': [
{'bleu': 32.46679154750991,
'attr_compression': 1.2,
'attr_copy_len': 2.0,
'attr_coverage': 0.8,
'attr_density': 2.0,
'attr_hypothesis_len': 5,
'attr_novelty': 0.5,
'attr_repetition': 0.0,
'attr_source_len': 6,
'chrf': 38.56890099861521}
],
'corpus_level': {
'corpus_bleu': 32.46679154750991,
'corpus_attr_compression': 1.2,
'corpus_attr_copy_len': 2.0,
'corpus_attr_coverage': 0.8,
'corpus_attr_density': 2.0,
'corpus_attr_hypothesis_len': 5.0,
'corpus_attr_novelty': 0.5,
'corpus_attr_repetition': 0.0,
'corpus_attr_source_len': 6.0,
'corpus_chrf': 38.56890099861521
}
}
Long-term TODO
- 完善功能
- 只给aws的ip (起一个api.eaas类似这样的域名)
- 打包成package
- metric corpus-level指标计算; BLEU corpus-level的计算检查(是否其他metric也有类似的);我们可能要设计下返回结果的json格式
- 我们弄个文档,总结每个指标的默认预处理方法,超参数使用,考虑是否预留个接口给用户设置
- Confidence interval计算功能
- Fine-grained analysis功能
- 优化API访问效率
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