Problems during run initial step

[simplelifetime]

Here is my running code:

import evaluate
ev = evaluate.load('exact_match')

Always return below error
Downloading builder script: 3.69kB [00:00, 4.56MB/s]
Traceback (most recent call last):
File "", line 1, in
File "/home/aka/anaconda3/lib/python3.10/site-packages/evaluate/loading.py", line 751, in load
evaluation_cls = import_main_class(evaluation_module.module_path)
File "/home/aka/anaconda3/lib/python3.10/site-packages/evaluate/loading.py", line 76, in import_main_class
module = importlib.import_module(module_path)
File "/home/aka/anaconda3/lib/python3.10/importlib/init.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "", line 1050, in _gcd_import
File "", line 1027, in _find_and_load
File "", line 1006, in _find_and_load_unlocked
File "", line 688, in _load_unlocked
File "", line 879, in exec_module
File "", line 1017, in get_code
File "", line 947, in source_to_code
File "", line 241, in _call_with_frames_removed
File "/home/aka/.cache/huggingface/modules/evaluate_modules/metrics/evaluate-metric--exact_match/009c8b5313309ea5b135d526433d5ee76508ba1554cbe88310a30f85bb57ec88/exact_match.py", line 16
}
^
SyntaxError: closing parenthesis '}' does not match opening parenthesis '(' on line 14

[BUAADreamer]

Have you solved this problem? I also encountered

[ErikaaWang]

Same here

[JingyangXiang]

Same here

[maktukmak]

Same here with version 0.4.2

[SJY8460]

Same problem

[ErikaaWang]

Hey guys, I guess it's a problem with evaluate==0.4.2. I replaced the cached exact_match.py script (the file where the error occured) with a previous version (sry not sure which version it is exactly), and everything works fine from my side. Please feel free to use this as a temperory solution, but I think some bug need to be solved within version 0.4.2.

The exact_match.py I used: (I obtained it from my partner, credit to him :)

# 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 1.0, 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"], 2))
    0.25

    >>> 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"], 2))
    0.5


    >>> 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"], 2))
    0.75

    >>> 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"], 2))
    1.0

    >>> 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"], 2))
    0.33
"""

_CITATION = """
"""


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class ExactMatch(evaluate.Metric):
    def _info(self):
        return evaluate.MetricInfo(
            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)}

[Shuizhimei]

Same problem. I tried the solution proposed by @ErikaaWang and found it worked.

[baifanxxx]

I can see this in blue.py

由于流量过大，本站暂不支持访问 Spaces 空间，可前往 Hugging Face 官网查看。

返回上一页 前往官网入口

<script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-4098528738253489" crossorigin="anonymous"></script> <script> (adsbygoogle = window.adsbygoogle || []).push({}); </script>

<script>
    function goBack() {
        window.history.back();
    }

    function redirectToOfficialApplicationPage() {
        const url = new URL(window.location.href);
        const path = url.pathname;
        const regex = /^\/spaces\/(.*)$/;
        const match = path.match(regex);

        if (match) {
            const officialPageUrl = `https://huggingface.co${path}`;
            window.location.href = officialPageUrl;
        } else {
            const officialPageUrl = `https://huggingface.co/spaces`;
            window.location.href = officialPageUrl;
        }
    }

    // Dynamically update the button text based on the 'next' URL parameter
    window.onload = () => {
        const url = new URL(window.location.href);
        const path = url.pathname;
        const regex = /^\/spaces\/(.*)$/;
        const match = path.match(regex);

        if (match) {
            const projectName = match[1];
            document.getElementById('applyButton').textContent = `前往官网 ${projectName} 入口`;
        }
    };
</script>

I guess the problem may be from the https://hf-mirror.com

[ZeguanXiao]

I fixed the issue by changing HF_ENDPOINT to https://huggingface.co/.

[Hanser14Forever]

Same problem, I tried the solution from @ZeguanXiaoand, but it didn't work out

[sxluo]

Same problem, I tried the solution from @ZeguanXiaoand, but it didn't work out

---

Try: export HF_ENDPOINT="https://huggingface.co"

[Hanser14Forever]

@sxluo Thanks for your advice!
I originally have this line:

export HF_ENDPOINT=https://hf-mirror.com

However I cannot remove this line, or change this to https://huggingface.co. Otherwise i wouldn't be able to connect to HF on my server. (mainland China).
So I clone 'evaluate' to my local path, and change this line:

metric = evaluate.load("accuracy")

to:

metric = evaluate.load(MY_PATH+"/evaluate/accuracy/accuracy.py")

It turns out to work well :>. Hope this could help others.