Update Space (evaluate main: 828c6327)

README.md

@@ -1,12 +1,117 @@
 ---
-title: Exact_match
-emoji: 🌍
-colorFrom: gray
-colorTo: purple
+title: Exact Match
+emoji: 🤗 
+colorFrom: blue
+colorTo: red
 sdk: gradio
 sdk_version: 3.0.2
 app_file: app.py
 pinned: false
+tags:
+- evaluate
+- metric
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+# Metric Card for Exact Match
+
+
+## Metric Description
+A given predicted string's exact match score is 1 if it is the exact same as its reference string, and is 0 otherwise.
+
+- **Example 1**: The exact match score of prediction "Happy Birthday!" is 0, given its reference is "Happy New Year!".
+- **Example 2**: The exact match score of prediction "The Colour of Magic (1983)" is 1, given its reference is also "The Colour of Magic (1983)".
+
+The exact match score of a set of predictions is the sum of all of the individual exact match scores in the set, divided by the total number of predictions in the set.
+
+- **Example**: The exact match score of the set {Example 1, Example 2} (above) is 0.5.
+
+
+## How to Use
+At minimum, this metric takes as input predictions and references:
+```python
+>>> from datasets import load
+>>> exact_match_metric = load("exact_match")
+>>> results = exact_match_metric.compute(predictions=predictions, references=references)
+```
+
+### Inputs
+- **`predictions`** (`list` of `str`): List of predicted texts.
+- **`references`** (`list` of `str`): List of reference texts.
+- **`regexes_to_ignore`** (`list` of `str`): Regex expressions of characters to ignore when calculating the exact matches. Defaults to `None`. Note: the regex changes are applied before capitalization is normalized.
+- **`ignore_case`** (`bool`): If `True`, turns everything to lowercase so that capitalization differences are ignored. Defaults to `False`.
+- **`ignore_punctuation`** (`bool`): If `True`, removes punctuation before comparing strings. Defaults to `False`.
+- **`ignore_numbers`** (`bool`): If `True`, removes all digits before comparing strings. Defaults to `False`.
+
+
+### Output Values
+This metric outputs a dictionary with one value: the average exact match score.
+
+```python
+{'exact_match': 100.0}
+```
+
+This metric's range is 0-100, inclusive. Here, 0.0 means no prediction/reference pairs were matches, while 100.0 means they all were.
+
+#### Values from Popular Papers
+The exact match metric is often included in other metrics, such as SQuAD. For example, the [original SQuAD paper](https://nlp.stanford.edu/pubs/rajpurkar2016squad.pdf) reported an Exact Match score of 40.0%. They also report that the human performance Exact Match score on the dataset was 80.3%.
+
+### Examples
+Without including any regexes to ignore:
+```python
+>>> exact_match = evaluate.load("exact_match")
+>>> refs = ["the cat", "theater", "YELLING", "agent007"]
+>>> preds = ["cat?", "theater", "yelling", "agent"]
+>>> results = exact_match.compute(references=refs, predictions=preds)
+>>> print(round(results["exact_match"], 1))
+25.0
+```
+
+Ignoring regexes "the" and "yell", as well as ignoring case and punctuation:
+```python
+>>> exact_match = evaluate.load("exact_match")
+>>> refs = ["the cat", "theater", "YELLING", "agent007"]
+>>> preds = ["cat?", "theater", "yelling", "agent"]
+>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
+>>> print(round(results["exact_match"], 1))
+50.0
+```
+Note that in the example above, because the regexes are ignored before the case is normalized, "yell" from "YELLING" is not deleted.
+
+Ignoring "the", "yell", and "YELL", as well as ignoring case and punctuation:
+```python
+>>> exact_match = evaluate.load("exact_match")
+>>> refs = ["the cat", "theater", "YELLING", "agent007"]
+>>> preds = ["cat?", "theater", "yelling", "agent"]
+>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
+>>> print(round(results["exact_match"], 1))
+75.0
+```
+
+Ignoring "the", "yell", and "YELL", as well as ignoring case, punctuation, and numbers:
+```python
+>>> exact_match = evaluate.load("exact_match")
+>>> refs = ["the cat", "theater", "YELLING", "agent007"]
+>>> preds = ["cat?", "theater", "yelling", "agent"]
+>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
+>>> print(round(results["exact_match"], 1))
+100.0
+```
+
+An example that includes sentences:
+```python
+>>> exact_match = evaluate.load("exact_match")
+>>> refs = ["The cat sat on the mat.", "Theaters are great.", "It's like comparing oranges and apples."]
+>>> preds = ["The cat sat on the mat?", "Theaters are great.", "It's like comparing apples and oranges."]
+>>> results = exact_match.compute(references=refs, predictions=preds)
+>>> print(round(results["exact_match"], 1))
+33.3
+```
+
+
+## Limitations and Bias
+This metric is limited in that it outputs the same score for something that is completely wrong as for something that is correct except for a single character. In other words, there is no award for being *almost* right.
+
+## Citation
+
+## Further References
+- Also used in the [SQuAD metric](https://github.com/huggingface/datasets/tree/master/metrics/squad) 

app.py

@@ -0,0 +1,6 @@
+import evaluate
+from evaluate.utils import launch_gradio_widget
+
+
+module = evaluate.load("exact_match")
+launch_gradio_widget(module)

exact_match.py

@@ -0,0 +1,137 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Exact Match metric."""
+import re
+import string
+
+import datasets
+import numpy as np
+
+import evaluate
+
+
+_DESCRIPTION = """
+Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
+"""
+
+_KWARGS_DESCRIPTION = """
+Args:
+    predictions: List of predicted texts.
+    references: List of reference texts.
+    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
+        ignore when calculating the exact matches. Note: these regexes are removed
+        from the input data before the changes based on the options below (e.g. ignore_case,
+        ignore_punctuation, ignore_numbers) are applied.
+    ignore_case: Boolean, defaults to False. If true, turns everything
+        to lowercase so that capitalization differences are ignored.
+    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
+        comparing predictions and references.
+    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
+        comparing predictions and references.
+Returns:
+    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
+Examples:
+    >>> exact_match = evaluate.load("exact_match")
+    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
+    >>> preds = ["cat?", "theater", "yelling", "agent"]
+    >>> results = exact_match.compute(references=refs, predictions=preds)
+    >>> print(round(results["exact_match"], 1))
+    25.0
+
+    >>> exact_match = evaluate.load("exact_match")
+    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
+    >>> preds = ["cat?", "theater", "yelling", "agent"]
+    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
+    >>> print(round(results["exact_match"], 1))
+    50.0
+
+
+    >>> exact_match = evaluate.load("exact_match")
+    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
+    >>> preds = ["cat?", "theater", "yelling", "agent"]
+    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
+    >>> print(round(results["exact_match"], 1))
+    75.0
+
+    >>> exact_match = evaluate.load("exact_match")
+    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
+    >>> preds = ["cat?", "theater", "yelling", "agent"]
+    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
+    >>> print(round(results["exact_match"], 1))
+    100.0
+
+    >>> exact_match = evaluate.load("exact_match")
+    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It's like comparing oranges and apples."]
+    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It's like comparing apples and oranges."]
+    >>> results = exact_match.compute(references=refs, predictions=preds)
+    >>> print(round(results["exact_match"], 1))
+    33.3
+
+"""
+
+_CITATION = """
+"""
+
+
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class ExactMatch(evaluate.EvaluationModule):
+    def _info(self):
+        return evaluate.EvaluationModuleInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            inputs_description=_KWARGS_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "predictions": datasets.Value("string", id="sequence"),
+                    "references": datasets.Value("string", id="sequence"),
+                }
+            ),
+            reference_urls=[],
+        )
+
+    def _compute(
+        self,
+        predictions,
+        references,
+        regexes_to_ignore=None,
+        ignore_case=False,
+        ignore_punctuation=False,
+        ignore_numbers=False,
+    ):
+
+        if regexes_to_ignore is not None:
+            for s in regexes_to_ignore:
+                predictions = np.array([re.sub(s, "", x) for x in predictions])
+                references = np.array([re.sub(s, "", x) for x in references])
+        else:
+            predictions = np.asarray(predictions)
+            references = np.asarray(references)
+
+        if ignore_case:
+            predictions = np.char.lower(predictions)
+            references = np.char.lower(references)
+
+        if ignore_punctuation:
+            repl_table = string.punctuation.maketrans("", "", string.punctuation)
+            predictions = np.char.translate(predictions, table=repl_table)
+            references = np.char.translate(references, table=repl_table)
+
+        if ignore_numbers:
+            repl_table = string.digits.maketrans("", "", string.digits)
+            predictions = np.char.translate(predictions, table=repl_table)
+            references = np.char.translate(references, table=repl_table)
+
+        score_list = predictions == references
+
+        return {"exact_match": np.mean(score_list) * 100}

requirements.txt

@@ -0,0 +1,3 @@
+# TODO: fix github to release
+git+https://github.com/huggingface/evaluate.git@b6e6ed7f3e6844b297bff1b43a1b4be0709b9671
+datasets~=2.0