NLP_Metrics

Install extra dependencies to compute LLM evaluation metrics

View Source on GitHub

minimum required for NLP Metrics

Install extra dependencies in the SDK:

!pip install arize[NLP_Metrics] 

Import metrics from arize.pandas.generative.nlp_metrics

Metrics

bleu

BLEU score is typically used to evaluate the quality of machine-translated text from one natural language to another. BLEU calculates scores for individual translated segments, typically sentences, by comparing them to a set of high-quality reference translations. These scores are then averaged over the entire corpus to obtain an estimate of the overall quality of the translation.

Argument	Expected Type	Description
response_col	pd.Series	[Required] Pandas Series containing translations (as strings) to score
references_col	pd.Series	[Required] Pandas Series containing a reference, or list of several references, per prediction
max_order	int	[Optional] Maximum n-gram order to use when computing BLEU score. Defaults to 4
smooth	bool	[Optional] Whether or not to apply Lin et al. 2004 smoothing. Defaults to False

Code Example

bleu_scores = bleu(
    response_col=df['summary'], 
    references_col=df['reference_summary'],
    max_order=5, # optional field
    smooth=True # optional field
    )

sacre_bleu

A hassle-free computation of shareable, comparable, and reproducible BLEU scores. Inspired by Rico Sennrich’s multi-bleu-detok.perl, it produces the official Workshop on Machine Translation (WMT) scores but works with plain text.

Argument	Expected Type	Description
response_col	pd.Series	[Required] Pandas Series containing translations (as strings) to score
references_col	pd.Series	[Required] Pandas Series containing a reference, or list of several references, per prediction Note: There must be the same number of references for each prediction (i.e. all sub-lists must be of the same length)
smooth_method	str	[Optional] The smoothing method to use, defaults to 'exp'. Possible values are: - 'none': no smoothing - 'floor': increment zero counts - 'add-k': increment num/denom by k for n>1 - 'exp': exponential decay
smooth_value	float	[Optional] The smoothing value. Defaults to None smooth_method='floor', smooth_value defaults to 0.1 smooth_method='add-k', smooth_value defaults to 1
lowercase	bool	[Optional] If True, lowercases the input, enabling case-insensitivity. Defaults to False
force	bool	[Optional] If True, insists that your tokenized input is actually de-tokenized. Defaults to False
use_effective_order	bool	[Optional] If True, stops including n-gram orders for which precision is 0. This should be True, if sentence-level BLEU will be computed. Defaults to False

Code Example

sacre_bleu_scores = sacre_bleu(
    response_col=df['summary'], 
    references_col=df['reference_summary'],
    smooth_method='floor', # optional field
    smooth_value=0.1, # defaulted to 0.1 since smooth_method is 'floor'
    lowercase=True # optional field
    )

google_bleu

BLEU score is typically used as a corpus measure, and it has some limitations when applied to single sentences. To overcome this issue in RL experiments, there exists a variation called the GLEU score. The GLEU score is the minimum of recall and precision.

Argument	Expected Type	Description
response_col	pd.Series	Pandas Series containing translations (as strings) to score
references_col	pd.Series	Pandas Series containing a reference, or list of several references, for each translation
min_len	int	The minimum order of n-gram this function should extract. Defaults to 1
max_len	int	The maximum order of n-gram this function should extract. Defaults to 4

Code Example

google_bleu_scores = google_bleu(
    response_col=df['summary'], 
    references_col=df['reference_summary'], 
    min_len=2, # optional field
    max_len=5 # optional field
    )

rouge

A software package and a set of metrics commonly used to evaluate machine translation and automatic summarization software in natural language processing. These metrics involve comparing a machine-produced summary or translation with a reference or set of references that have been human-produced.

Argument	Expected Type	Description
response_col	pd.Series	[Required] Pandas Series containing predictions (as strings) to score
references_col	pd.Series	[Required] Pandas Series containing a reference, or list of several references, per prediction
rogue_types	List[str]	[Optional] A list of rouge types to calculate. Defaults to ['rougeL']. Valid rogue types: -rogue1: unigram (1-gram) based scoring -rogue2: bigram (2-gram) based scoring -rogueL: longest common subsequence based scoring -rogueLSum: splits text using '/n'
use_stemmer	bool	[Optional] If True, uses Porter stemmer to strip word suffixes. Defaults to False.

Code Example

rouge_scores = rouge(
    response_col=df['summary'], 
    references_col=df['reference_summary'], 
    rouge_types=['rouge1', 'rouge2', 'rougeL', 'rougeLsum'] # optional field
    )

meteor

An automatic metric typically used to evaluate machine translation, which is based on a generalized concept of unigram matching between the machine-produced translation and the reference human-produced translations.

Argument	Expected Type	Description
response_col	pd.Series	[Required] Pandas Series containing predictions (as strings) to score
references_col	pd.Series	[Required] Pandas Series containing a reference, or list of several references, per prediction
alpha	float	[Optional] Parameter for controlling relative weights of precision and recall. Default is 0.9
beta	float	[Optional] Parameter for controlling shape of penalty as a function of fragmentation. Default is 3
gamma	float	[Optional] The relative weight assigned to fragmentation penalty. Default is 0.5

Code Example

meteor_scores = meteor(
    response_col=df['summary'], 
    references_col=df['reference_summary'], 
    alpha=0.8, # optional field
    beta=4, # optional field
    gamma=0.4 # optional field
    )