metadata

title: relation_extraction
datasets:
  - none
tags:
  - evaluate
  - metric
description: >-
  This metric is used for evaluating the F1 accuracy of input references and
  predictions.
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
license: apache-2.0

Metric Card for relation_extraction evalutation

This metric is used for evaluating the quality of relation extraction output. By calculating the Micro and Macro F1 score of every relation extraction outputs to ensure the quality.

Metric Description

This metric can be used in relation extraction evaluation.

How to Use

This metric takes 2 inputs, prediction and references(ground truth). Both of them are a list of list of dictionary of entity's name and entity's type:

import evaluate
metric_path = "Ikala-allen/relation_extraction"
module = evaluate.load(metric_path)
references = [
  [
    {"head": "phip igments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
  ]
]
predictions = [
  [
    {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
  ]
]
evaluation_scores = module.compute(predictions=predictions, references=references)

Inputs

predictions (list of list of dictionary): relation and its type of prediction
references (list of list of dictionary): references for each relation and its type
mode (str): define strict or boundaries mode
only_all (bool): define whether only output ["ALL"] relation_type score or every relation_type score, default True
relation_types (list): define relation type that need to be evaluate, if not given, it will construct relation_types from ground truth, default []

Output Values

output (dictionary of dictionarys) with multiple key-value pairs

sell (dictionary): score of type sell
- tp : true positive count
- fp : false positive count
- fn : false negative count
- p : precision
- r : recall
- f1 : micro f1 score
ALL (dictionary): score of all of the type (sell and belongs to)
- tp : true positive count
- fp : false positive count
- fn : false negative count
- p : precision
- r : recall
- f1 : micro f1 score
- Macro_f1 : macro f1 score
- Macro_p : macro precision
- Macro_r : macro recall

Output Example:

{'tp': 1, 'fp': 1, 'fn': 1, 'p': 50.0, 'r': 50.0, 'f1': 50.0, 'Macro_f1': 50.0, 'Macro_p': 50.0, 'Macro_r': 50.0}

Remind : Macro_f1、Macro_p、Macro_r、p、r、f1 are always a number between 0 and 1. And tp、fp、fn depend on how many data inputs.

Examples

Example of only one prediction and reference:

metric_path = "Ikala-allen/relation_extraction"
module = evaluate.load(metric_path)
references = [
  [
    {"head": "phip igments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
  ]
]
predictions = [
  [
    {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
  ]
]
evaluation_scores = module.compute(predictions=predictions, references=references)
print(evaluation_scores)
>>> {'sell': {'tp': 1, 'fp': 1, 'fn': 1, 'p': 50.0, 'r': 50.0, 'f1': 50.0}, 'ALL': {'tp': 1, 'fp': 1, 'fn': 1, 'p': 50.0, 'r': 50.0, 'f1': 50.0, 'Macro_f1': 50.0, 'Macro_p': 50.0, 'Macro_r': 50.0}}

Example with two or more prediction and reference:

>>> metric_path = "Ikala-allen/relation_extraction"
>>> module = evaluate.load(metric_path)
>>> references = [
...     [
...         {"head": "phip igments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
...         {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
...     ],[
...           {'head': 'SABONTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'}
...     ]
...   ]
>>> predictions = [
...    [
...        {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
...        {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
...    ],[
...          {'head': 'SABONTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'},
...          {'head': 'SNTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'}  
...    ]
...  ]
>>> evaluation_scores = module.compute(predictions=predictions, references=references)  
>>> print(evaluation_scores)  
{'sell': {'tp': 2, 'fp': 2, 'fn': 1, 'p': 50.0, 'r': 66.66666666666667, 'f1': 57.142857142857146}, 'ALL': {'tp': 2, 'fp': 2, 'fn': 1, 'p': 50.0, 'r': 66.66666666666667, 'f1': 57.142857142857146, 'Macro_f1': 57.142857142857146, 'Macro_p': 50.0, 'Macro_r': 66.66666666666667}}

Limitations and Bias

This metric has strict filter mechanism, if any of the prediction's entity names, such as head, head_type, type, tail, or tail_type, is not exactly the same as the reference one. It will count as fp or fn.

Citation

@Paper{
    author = {Bruno Taillé, Vincent Guigue, Geoffrey Scoutheeten, Patrick Gallinari},
    title = {Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!},
    year = {2020},
    link = https://arxiv.org/abs/2009.10684
}

Further References

This evaluation metric implementation uses https://github.com/btaille/sincere/blob/master/code/utils/evaluation.py