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All Metrics 📉
All metrics from our nlp repository and community bucket.
Also check out the list of Datasets 🏷.
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. It has been shown to correlate with human judgment on sentence-level and system-level evaluation. Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language generation tasks. See the [README.md] file at https://github.com/Tiiiger/bert_score for more information.
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and remains one of the most popular automated and inexpensive metrics. Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations. Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness are not taken into account[citation needed]. BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1 representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional reference translations will increase the BLEU score.
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018) and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune it for your specific application (the latter is expected to perform better). See the [README.md] file at https://github.com/google-research/bleurt for more information.
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which implements of the common evaluation metrics including MUC [Vilain et al, 1995], B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005], LEA [Moosavi and Strube, 2016] and the averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe) [Denis and Baldridge, 2009a; Pradhan et al., 2011]. This wrapper of CoVal currently only work with CoNLL line format: The CoNLL format has one word per line with all the annotation for this word in column separated by spaces: Column Type Description 1 Document ID This is a variation on the document filename 2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc. 3 Word number 4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release. 5 Part-of-Speech 6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. 7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-" 8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7. 9 Word sense This is the word sense of the word in Column 3. 10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data. 11 Named Entities These columns identifies the spans representing various named entities. 12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7. N Coreference Coreference chain information encoded in a parenthesis structure. More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md CoVal code was written by @ns-moosavi. Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py The test suite is taken from https://github.com/conll/reference-coreference-scorers/ Mention evaluation and the test suite are added by @andreasvc. Parsing CoNLL files is developed by Leo Born.
The GLEU metric is a variant of BLEU proposed for evaluating grammatical error corrections using n-gram overlap with a set of reference sentences, as opposed to precision/recall of specific annotated errors (Napoles et al., 2015). GLEU hews more closely to human judgments than the rankings produced by metrics such as MaxMatch and I-measure. The present metric is the second version of GLEU (Napoles et al., 2016) modified to address problems that arise when using an increasing number of reference sets. The modified metric does not require tuning and is recommended to be used instead of the original version.
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for evaluating automatic summarization and machine translation software in natural language processing. The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation. This metrics is a wrapper around Google Research reimplementation of ROUGE: https://github.com/google-research/google-research/tree/master/rouge
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores. Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text. It also knows all the standard test sets and handles downloading, processing, and tokenization for you. See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
seqeval is a Python framework for sequence labeling evaluation. seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on. This is well-tested by using the Perl script conlleval, which can be used for measuring the performance of a system that has processed the CoNLL-2000 shared task data. seqeval supports following formats: IOB1 IOB2 IOE1 IOE2 IOBES See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD). Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
This metric wrap the official scoring script for version 2 of the Stanford Question Answering Dataset (SQuAD). Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.
XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).