eaas 0.3.7

Evaluation as a Service for Natural Language Processing

Evaluation-as-a-Service for NLP

Usage

Before using EaaS, please see the terms of use. Detailed documentation can be found here. To install the EaaS, simply run

pip install eaas

Run your "Hello, world"

A minimal EaaS application looks something like this:

from eaas import Config, Client

client = Client(Config())

inputs = [{
    "source": "Hello, my world",
    "references": ["Hello, world", "Hello my world"],
    "hypothesis": "Hi, my world"
}]
metrics = ["rouge1", "bleu", "chrf"]

score_dic = client.score(inputs, metrics=metrics)

If eaas has been installed successfully, you should get the results below by printing score_dic. Each entry corresponds to the metrics passed to metrics (in the same order). The corpus entry indicates the corpus-level score, sample entry is a list of sample-level scores:

score_dic = {'scores':
     [
         {'corpus': 0.6666666666666666, 'sample': [0.6666666666666666]},
         {'corpus': 0.35355339059327373, 'sample': [0.35355339059327373]},
         {'corpus': 0.4900623006253688, 'sample': [0.4900623006253688]}
     ]
}

This is a list of the results

Notably:

• 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.
• There are other parameters that can be set in the score function: task is the name of task (for calculating attributes), metrics is metric list, lang is the two-letter code language, cal_attributes is an indicator that decides whether to calculate some task-dependent attributes.

Supported Metrics

Currently, EaaS supports the following metrics:

• bart_score_en_ref: BARTScore is a sequence to sequence framework based on pre-trained language model BART. bart_score_cnn_hypo_ref uses the CNNDM finetuned BART. It calculates the average generation score of Score(hypothesis|reference) and Score(reference|hypothesis).
• bart_score_en_src: BARTScore using the CNNDM finetuned BART. It calculates Score(hypothesis|source).
• bert_score_p: BERTScore is a metric designed for evaluating translated text using BERT-based matching framework. bert_score_p calculates the BERTScore precision.
• bert_score_r: BERTScore recall.
• bert_score_f: BERTScore f score.
• bleu: BLEU measures modified ngram matches between each candidate translation and the reference translations.
• chrf: CHRF measures the character-level ngram matches between hypothesis and reference.
• comet: COMET is a neural framework for training multilingual machine translation evaluation models. comet uses the wmt20-comet-da checkpoint which utilizes source, hypothesis and reference.
• comet_qe: COMET for quality estimation. comet_qe uses the wmt20-comet-qe-da checkpoint which utilizes only source and hypothesis.
• mover_score: MoverScore is a metric similar to BERTScore. Different from BERTScore, it uses the Earth Mover’s Distance instead of the Euclidean Distance.
• prism: PRISM is a sequence to sequence framework trained from scratch. prism calculates the average generation score of Score(hypothesis|reference) and Score(reference|hypothesis).
• prism_qe: PRISM for quality estimation. It calculates Score(hypothesis| source).
• rouge1: ROUGE-1 refers to the overlap of unigram (each word) between the system and reference summaries.
• rouge2: ROUGE-2 refers to the overlap of bigrams between the system and reference summaries.
• rougeL: ROUGE-L refers to the longest common subsequence between the system and reference summaries.

The default configurations for each metric can refer to this doc

Asynchronous Requests

If you want to make a call to the EaaS server to calculate some metrics and continue local computation while waiting for the result, you can do so as follows:

from eaas import Config
from eaas.async_client import AsyncClient

config = Config()
client = AsyncClient(config)

inputs = ...
req = client.async_score(inputs, metrics=["bleu"])
# do some other computation
result = req.get_result()