模型:

1-800-BAD-CODE/xlm-roberta_punctuation_fullstop_truecase

英文

模型概述

这是一个基于xlm-roberta模型进行微调的模型,用于在47种语言中还原标点、恢复正确的大小写,并检测句子边界(句号)。

使用方法

如果你只想尝试一下该模型,此页面上的小部件就足够了。如果要离线使用该模型,以下代码段展示了如何使用包装器(由我编写,在PyPI上可获得)和手动方式(使用此存储库中的ONNX和SentencePiece模型)使用模型。

使用 punctuators 包

点击查看使用包装器的方法

使用该模型最简单的方法是安装 punctuators 包:

$ pip install punctuators

但这只是一个ONNX模型和SentencePiece模型,所以您可以根据需要进行任意使用。

punctuators API 的输入是一个字符串列表(批处理)。每个字符串将被用于标点还原、恢复正确的大小写,并根据预测的句号进行分割。因此,输出将是一个字符串列表的列表:每个输入文本对应一个分割后的句子列表。需要禁用句号功能,请使用 m.infer(texts, apply_sbd=False)。输出将是一个字符串列表:每个输入文本对应一个标点还原、恢复正确大小写后的字符串。

例如使用
from typing import List

from punctuators.models import PunctCapSegModelONNX

m: PunctCapSegModelONNX = PunctCapSegModelONNX.from_pretrained(
    "1-800-BAD-CODE/xlm-roberta_punctuation_fullstop_truecase"
)

input_texts: List[str] = [
    "hola mundo cómo estás estamos bajo el sol y hace mucho calor santa coloma abre los huertos urbanos a las escuelas de la ciudad",
    "hello friend how's it going it's snowing outside right now in connecticut a large storm is moving in",
    "未來疫苗將有望覆蓋3歲以上全年齡段美國與北約軍隊已全部撤離還有鐵路公路在內的各項基建的來源都將枯竭",
    "በባለፈው ሳምንት ኢትዮጵያ ከሶማሊያ 3 ሺህ ወታደሮቿንም እንዳስወጣች የሶማሊያው ዳልሳን ሬድዮ ዘግቦ ነበር ጸጥታ ሃይሉና ህዝቡ ተቀናጅቶ በመስራቱ በመዲናዋ ላይ የታቀደው የጥፋት ሴራ ከሽፏል",
    "こんにちは友人" "調子はどう" "今日は雨の日でしたね" "乾いた状態を保つために一日中室内で過ごしました",
    "hallo freund wie geht's es war heute ein regnerischer tag nicht wahr ich verbrachte den tag drinnen um trocken zu bleiben",
    "हैलो दोस्त ये कैसा चल रहा है आज बारिश का दिन था न मैंने सूखा रहने के लिए दिन घर के अंदर बिताया",
    "كيف تجري الامور كان يومًا ممطرًا اليوم أليس كذلك قضيت اليوم في الداخل لأظل جافًا",
]

results: List[List[str]] = m.infer(
    texts=input_texts, apply_sbd=True,
)
for input_text, output_texts in zip(input_texts, results):
    print(f"Input: {input_text}")
    print(f"Outputs:")
    for text in output_texts:
        print(f"\t{text}")
    print()
预期输出
Input: hola mundo cómo estás estamos bajo el sol y hace mucho calor santa coloma abre los huertos urbanos a las escuelas de la ciudad
Outputs:
    Hola mundo, ¿cómo estás?
    Estamos bajo el sol y hace mucho calor.
    Santa Coloma abre los huertos urbanos a las escuelas de la ciudad.

Input: hello friend how's it going it's snowing outside right now in connecticut a large storm is moving in
Outputs:
    Hello friend, how's it going?
    It's snowing outside right now.
    In Connecticut, a large storm is moving in.

Input: 未來疫苗將有望覆蓋3歲以上全年齡段美國與北約軍隊已全部撤離還有鐵路公路在內的各項基建的來源都將枯竭
Outputs:
    未來,疫苗將有望覆蓋3歲以上全年齡段。
    美國與北約軍隊已全部撤離。
    還有,鐵路,公路在內的各項基建的來源都將枯竭。

Input: በባለፈው ሳምንት ኢትዮጵያ ከሶማሊያ 3 ሺህ ወታደሮቿንም እንዳስወጣች የሶማሊያው ዳልሳን ሬድዮ ዘግቦ ነበር ጸጥታ ሃይሉና ህዝቡ ተቀናጅቶ በመስራቱ በመዲናዋ ላይ የታቀደው የጥፋት ሴራ ከሽፏል
Outputs:
    በባለፈው ሳምንት ኢትዮጵያ ከሶማሊያ 3 ሺህ ወታደሮቿንም እንዳስወጣች የሶማሊያው ዳልሳን ሬድዮ ዘግቦ ነበር።
    ጸጥታ ሃይሉና ህዝቡ ተቀናጅቶ በመስራቱ በመዲናዋ ላይ የታቀደው የጥፋት ሴራ ከሽፏል።

Input: こんにちは友人調子はどう今日は雨の日でしたね乾いた状態を保つために一日中室内で過ごしました
Outputs:
    こんにちは、友人、調子はどう?
    今日は雨の日でしたね。
    乾いた状態を保つために、一日中、室内で過ごしました。

Input: hallo freund wie geht's es war heute ein regnerischer tag nicht wahr ich verbrachte den tag drinnen um trocken zu bleiben
Outputs:
    Hallo Freund, wie geht's?
    Es war heute ein regnerischer Tag, nicht wahr?
    Ich verbrachte den Tag drinnen, um trocken zu bleiben.

Input: हैलो दोस्त ये कैसा चल रहा है आज बारिश का दिन था न मैंने सूखा रहने के लिए दिन घर के अंदर बिताया
Outputs:
    हैलो दोस्त, ये कैसा चल रहा है?
    आज बारिश का दिन था न, मैंने सूखा रहने के लिए दिन घर के अंदर बिताया।

Input: كيف تجري الامور كان يومًا ممطرًا اليوم أليس كذلك قضيت اليوم في الداخل لأظل جافًا
Outputs:
    كيف تجري الامور؟
    كان يومًا ممطرًا اليوم، أليس كذلك؟
    قضيت اليوم في الداخل لأظل جافًا.

手动使用

如果您想在没有包装器的情况下使用 ONNX 和 SP 模型,请参考以下示例。

点击查看手动使用
from typing import List

import numpy as np
import onnxruntime as ort
from huggingface_hub import hf_hub_download
from omegaconf import OmegaConf
from sentencepiece import SentencePieceProcessor

# Download the models from HF hub. Note: to clean up, you can find these files in your HF cache directory
spe_path = hf_hub_download(repo_id="1-800-BAD-CODE/xlm-roberta_punctuation_fullstop_truecase", filename="sp.model")
onnx_path = hf_hub_download(repo_id="1-800-BAD-CODE/xlm-roberta_punctuation_fullstop_truecase", filename="model.onnx")
config_path = hf_hub_download(
    repo_id="1-800-BAD-CODE/xlm-roberta_punctuation_fullstop_truecase", filename="config.yaml"
)

# Load the SP model
tokenizer: SentencePieceProcessor = SentencePieceProcessor(spe_path)  # noqa
# Load the ONNX graph
ort_session: ort.InferenceSession = ort.InferenceSession(onnx_path)
# Load the model config with labels, etc.
config = OmegaConf.load(config_path)
# Potential classification labels before each subtoken
pre_labels: List[str] = config.pre_labels
# Potential classification labels after each subtoken
post_labels: List[str] = config.post_labels
# Special class that means "predict nothing"
null_token = config.get("null_token", "<NULL>")
# Special class that means "all chars in this subtoken end with a period", e.g., "am" -> "a.m."
acronym_token = config.get("acronym_token", "<ACRONYM>")
# Not used in this example, but if your sequence exceed this value, you need to fold it over multiple inputs
max_len = config.max_length
# For reference only, graph has no language-specific behavior
languages: List[str] = config.languages

# Encode some input text, adding BOS + EOS
input_text = "hola mundo cómo estás estamos bajo el sol y hace mucho calor santa coloma abre los huertos urbanos a las escuelas de la ciudad"
input_ids = [tokenizer.bos_id()] + tokenizer.EncodeAsIds(input_text) + [tokenizer.eos_id()]

# Create a numpy array with shape [B, T], as the graph expects as input.
# Note that we do not pass lengths to the graph; if you are using a batch, padding should be tokenizer.pad_id() and the
# graph's attention mechanisms will ignore pad_id() without requiring explicit sequence lengths.
input_ids_arr: np.array = np.array([input_ids])

# Run the graph, get outputs for all analytics
pre_preds, post_preds, cap_preds, sbd_preds = ort_session.run(None, {"input_ids": input_ids_arr})
# Squeeze off the batch dimensions and convert to lists
pre_preds = pre_preds[0].tolist()
post_preds = post_preds[0].tolist()
cap_preds = cap_preds[0].tolist()
sbd_preds = sbd_preds[0].tolist()

# Segmented sentences
output_texts: List[str] = []
# Current sentence, which is built until we hit a sentence boundary prediction
current_chars: List[str] = []
# Iterate over the outputs, ignoring the first (BOS) and final (EOS) predictions and tokens
for token_idx in range(1, len(input_ids) - 1):
    token = tokenizer.IdToPiece(input_ids[token_idx])
    # Simple SP decoding
    if token.startswith("▁") and current_chars:
        current_chars.append(" ")
    # Token-level predictions
    pre_label = pre_labels[pre_preds[token_idx]]
    post_label = post_labels[post_preds[token_idx]]
    # If we predict "pre-punct", insert it before this token
    if pre_label != null_token:
        current_chars.append(pre_label)
    # Iterate over each char. Skip SP's space token,
    char_start = 1 if token.startswith("▁") else 0
    for token_char_idx, char in enumerate(token[char_start:], start=char_start):
        # If this char should be capitalized, apply upper case
        if cap_preds[token_idx][token_char_idx]:
            char = char.upper()
        # Append char
        current_chars.append(char)
        # if this is an acronym, add a period after every char (p.m., a.m., etc.)
        if post_label == acronym_token:
            current_chars.append(".")
    # Maybe this subtoken ends with punctuation
    if post_label != null_token and post_label != acronym_token:
        current_chars.append(post_label)

    # If this token is a sentence boundary, finalize the current sentence and reset
    if sbd_preds[token_idx]:
        output_texts.append("".join(current_chars))
        current_chars.clear()

# Maybe push final sentence, if the final token was not classified as a sentence boundary
if current_chars:
    output_texts.append("".join(current_chars))

# Pretty print
print(f"Input: {input_text}")
print("Outputs:")
for text in output_texts:
    print(f"\t{text}")

预期输出:

Input: hola mundo cómo estás estamos bajo el sol y hace mucho calor santa coloma abre los huertos urbanos a las escuelas de la ciudad
Outputs:
    Hola mundo, ¿cómo estás?
    Estamos bajo el sol y hace mucho calor.
    Santa Coloma abre los huertos urbanos a las escuelas de la ciudad.

模型架构

该模型实现了以下图表,能够在没有特定语言行为的情况下在任何语言中进行标点还原、恢复正确的大小写和分割句子:

点击查看图表说明

我们首先对文本进行标记化,并使用XLM-Roberta进行编码,这是该图表的预训练部分。

然后我们在每个子标记之前和之后预测标点。在每个标记之前进行预测,可以处理西班牙语中的倒置问号。在每个标记之后进行预测,可以处理其他所有标点,包括连续脚本语言和缩写词中的标点。

我们使用嵌入来表示预测标点标记,以通知句子边界头部将插入到文本中的标点。这样可以正确预测句号,因为某些标点标记(句号、问号等)与句子边界存在强相关性。

然后我们将句号的预测结果向右移动一个位置,以通知大小写恢复头部每个新句子的开头在哪里。这一点很重要,因为大小写恢复与句子边界强相关。

对于正确的大小写恢复,我们对于每个子标记预测 N 个预测结果,其中 N 是子标记的字符数。实际上,N 是最大子标记长度,多余的预测结果将被忽略。基本上,正确的大小写恢复被建模为一个多标签问题。这样可以进行任意字符的大写,例如 "NATO"、"MacDonald"、"mRNA" 等。

将所有这些预测应用于输入文本,我们可以在任何语言中进行标点还原、正确的大小写恢复和分割句子。

分词器

点击查看如何修复 XLM-Roberta 分词器的问题

不同于 FairSeq 使用的不合理的包装器以及 HuggingFace 奇怪地转移(未修复)的包装器,xlm-roberta SentencePiece 模型已经进行了调整,以正确编码文本。根据HuggingFace的评论:

# Original fairseq vocab and spm vocab must be "aligned":
# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

使用以下代码片段来修复 SentencePiece 模型的包装问题(如果有问题,请更正):

from sentencepiece import SentencePieceProcessor
from sentencepiece.sentencepiece_model_pb2 import ModelProto

m = ModelProto()
m.ParseFromString(open("/path/to/xlmroberta/sentencepiece.bpe.model", "rb").read())

pieces = list(m.pieces)
pieces = (
    [
        ModelProto.SentencePiece(piece="<s>", type=ModelProto.SentencePiece.Type.CONTROL),
        ModelProto.SentencePiece(piece="<pad>", type=ModelProto.SentencePiece.Type.CONTROL),
        ModelProto.SentencePiece(piece="</s>", type=ModelProto.SentencePiece.Type.CONTROL),
        ModelProto.SentencePiece(piece="<unk>", type=ModelProto.SentencePiece.Type.UNKNOWN),
    ]
    + pieces[3:]
    + [ModelProto.SentencePiece(piece="<mask>", type=ModelProto.SentencePiece.Type.USER_DEFINED)]
)
del m.pieces[:]
m.pieces.extend(pieces)

with open("/path/to/new/sp.model", "wb") as f:
    f.write(m.SerializeToString())

现在我们可以只使用 SentencePiece 模型而不需要包装器了。

标点后的特殊标记

该模型在每个子标记后预测以下一组标点符号标记:

Token Description Relevant Languages
<NULL> No punctuation All
<ACRONYM> Every character in this subword is followed by a period Primarily English, some European
. Latin full stop Many
, Latin comma Many
? Latin question mark Many
Full-width question mark Chinese, Japanese
Full-width comma Chinese, Japanese
Full-width full stop Chinese, Japanese
Ideographic comma Chinese, Japanese
Middle dot Japanese
Danda Hindi, Bengali, Oriya
؟ Arabic question mark Arabic
; Greek question mark Greek
Ethiopic full stop Amharic
Ethiopic comma Amharic
Ethiopic question mark Amharic

标点前的特殊标记

该模型在每个子单词前预测以下一组标点符号标记:

Token Description Relevant Languages
<NULL> No punctuation All
¿ Inverted question mark Spanish

训练细节

该模型在 NeMo 框架上使用 A100 GPU 进行了约 7 小时的训练。您可以在 tensorboard.dev 上查看 tensorboard 日志。

该模型使用来自 WMT 的 News Crawl 数据进行训练。每种语言使用了超过 100 万行文本,除了一些低资源语言可能使用了较少数据。语言选择基于 News Crawl 语料库是否包含了足够可靠的数据,作者对此进行了判断。

限制

该模型是在新闻数据上训练的,在对话或非正式数据上的表现可能不好。

该模型的生产质量不高。每种语言仅使用了约 100 万行数据进行训练,开发集可能由于网络抓取的新闻数据的性质而有噪音。

该模型过度预测了西班牙语的问号,特别是倒置问号 ¿(请参见下面的指标)。由于 ¿ 是一个罕见的标记,特别是在一个包含 47 种语言的模型中,通过从额外的未使用的训练数据中选择更多的这些句子进行数据过采样来解决这个问题,但这似乎使问题出现了“过校正”,预测出了很多西班牙语问号。

该模型可能会过度预测逗号。

如果您发现任何未提及的一般限制,请让我知道,以便下一次微调时可以解决所有限制。

评估

在这些指标中,请记住:

  • 数据存在噪音
  • 句子边界和大小写恢复是基于预测的标点进行条件训练的,这是最困难的任务,有时会出现错误。当基于引用标点进行条件训练时,对于大多数语言,准确率实际上达到了 100%。
  • 标点可以是主观的。例如,"Hola mundo, ¿cómo estás?" 或者 "Hola mundo. ¿Cómo estás?" 当句子较长且更实际时,这种歧义就会增多,影响所有三个指标。
  • 测试数据和示例生成

    每个测试示例是使用以下过程生成的:

  • 连接 11 个随机句子(测试集中的每个句子都有 1 个句子 + 10 个句子)
  • 将连接的句子转换为小写
  • 删除所有标点符号
  • 随着小写字母的增加和标点的删除,生成目标结果。

    数据是 News Crawl 的一部分,已进行去重处理。每种语言使用了需选的 3,000 行数据,每个数据由 11 个句子构成。我们生成了 3,000 个示例,其中第 i 个示例以第 i 个句子开头,并后跟从这 3,000 个句子测试集中随机选择的 10 个句子。

    为了测量大小写恢复和句子边界检测,使用了引用标点作为条件(参见上述图表)。如果使用预测标点,那么不正确的标点将导致大小写恢复和句子边界无法正确对齐,这些指标将被人为降低。

    选定语言的评估报告

    目前,以下是几种选定语言的指标报告。由于收集和漂亮打印 47 种语言的指标需要大量工作,我将逐步添加更多报告。

    展开以下任意标签,查看该语言的指标报告。

    英语

    punct_post test report: 
        label                                                precision    recall       f1           support   
        <NULL> (label_id: 0)                                    99.25      98.43      98.84     564908
        <ACRONYM> (label_id: 1)                                 63.14      84.67      72.33        613
        . (label_id: 2)                                         90.97      93.91      92.42      32040
        , (label_id: 3)                                         73.95      84.32      78.79      24271
        ? (label_id: 4)                                         79.05      81.94      80.47       1041
        ? (label_id: 5)                                          0.00       0.00       0.00          0
        , (label_id: 6)                                          0.00       0.00       0.00          0
        。 (label_id: 7)                                          0.00       0.00       0.00          0
        、 (label_id: 8)                                          0.00       0.00       0.00          0
        ・ (label_id: 9)                                          0.00       0.00       0.00          0
        । (label_id: 10)                                         0.00       0.00       0.00          0
        ؟ (label_id: 11)                                         0.00       0.00       0.00          0
        ، (label_id: 12)                                         0.00       0.00       0.00          0
        ; (label_id: 13)                                         0.00       0.00       0.00          0
        ። (label_id: 14)                                         0.00       0.00       0.00          0
        ፣ (label_id: 15)                                         0.00       0.00       0.00          0
        ፧ (label_id: 16)                                         0.00       0.00       0.00          0
        -------------------
        micro avg                                               97.60      97.60      97.60     622873
        macro avg                                               81.27      88.65      84.57     622873
        weighted avg                                            97.77      97.60      97.67     622873
    
    cap test report: 
        label                                                precision    recall       f1           support   
        LOWER (label_id: 0)                                     99.72      99.85      99.78    2134956
        UPPER (label_id: 1)                                     96.33      93.52      94.91      91996
        -------------------
        micro avg                                               99.59      99.59      99.59    2226952
        macro avg                                               98.03      96.68      97.34    2226952
        weighted avg                                            99.58      99.59      99.58    2226952
    
    seg test report: 
        label                                                precision    recall       f1           support   
        NOSTOP (label_id: 0)                                    99.99      99.98      99.99     591540
        FULLSTOP (label_id: 1)                                  99.61      99.89      99.75      34333
        -------------------
        micro avg                                               99.97      99.97      99.97     625873
        macro avg                                               99.80      99.93      99.87     625873
        weighted avg                                            99.97      99.97      99.97     625873
    

    西班牙语

      punct_pre test report: 
        label                                                precision    recall       f1           support   
        <NULL> (label_id: 0)                                    99.94      99.89      99.92     636941
        ¿ (label_id: 1)                                         56.73      71.35      63.20       1288
        -------------------
        micro avg                                               99.83      99.83      99.83     638229
        macro avg                                               78.34      85.62      81.56     638229
        weighted avg                                            99.85      99.83      99.84     638229
    
    punct_post test report: 
        label                                                precision    recall       f1           support   
        <NULL> (label_id: 0)                                    99.19      98.41      98.80     578271
        <ACRONYM> (label_id: 1)                                 30.10      56.36      39.24         55
        . (label_id: 2)                                         91.92      93.12      92.52      30856
        , (label_id: 3)                                         72.98      82.44      77.42      27761
        ? (label_id: 4)                                         52.77      71.85      60.85       1286
        ? (label_id: 5)                                          0.00       0.00       0.00          0
        , (label_id: 6)                                          0.00       0.00       0.00          0
        。 (label_id: 7)                                          0.00       0.00       0.00          0
        、 (label_id: 8)                                          0.00       0.00       0.00          0
        ・ (label_id: 9)                                          0.00       0.00       0.00          0
        । (label_id: 10)                                         0.00       0.00       0.00          0
        ؟ (label_id: 11)                                         0.00       0.00       0.00          0
        ، (label_id: 12)                                         0.00       0.00       0.00          0
        ; (label_id: 13)                                         0.00       0.00       0.00          0
        ። (label_id: 14)                                         0.00       0.00       0.00          0
        ፣ (label_id: 15)                                         0.00       0.00       0.00          0
        ፧ (label_id: 16)                                         0.00       0.00       0.00          0
        -------------------
        micro avg                                               97.40      97.40      97.40     638229
        macro avg                                               69.39      80.44      73.77     638229
        weighted avg                                            97.60      97.40      97.48     638229
    
    cap test report: 
        label                                                precision    recall       f1           support   
        LOWER (label_id: 0)                                     99.82      99.86      99.84    2324724
        UPPER (label_id: 1)                                     95.92      94.70      95.30      79266
        -------------------
        micro avg                                               99.69      99.69      99.69    2403990
        macro avg                                               97.87      97.28      97.57    2403990
        weighted avg                                            99.69      99.69      99.69    2403990
    
    seg test report: 
        label                                                precision    recall       f1           support   
        NOSTOP (label_id: 0)                                    99.99      99.96      99.98     607057
        FULLSTOP (label_id: 1)                                  99.31      99.88      99.60      34172
        -------------------
        micro avg                                               99.96      99.96      99.96     641229
        macro avg                                               99.65      99.92      99.79     641229
        weighted avg                                            99.96      99.96      99.96     641229
    

    阿姆哈拉语

    punct_post test report: 
        label                                                precision    recall       f1           support   
        <NULL> (label_id: 0)                                    99.83      99.28      99.56     729664
        <ACRONYM> (label_id: 1)                                  0.00       0.00       0.00          0
        . (label_id: 2)                                          0.00       0.00       0.00          0
        , (label_id: 3)                                          0.00       0.00       0.00          0
        ? (label_id: 4)                                          0.00       0.00       0.00          0
        ? (label_id: 5)                                          0.00       0.00       0.00          0
        , (label_id: 6)                                          0.00       0.00       0.00          0
        。 (label_id: 7)                                          0.00       0.00       0.00          0
        、 (label_id: 8)                                          0.00       0.00       0.00          0
        ・ (label_id: 9)                                          0.00       0.00       0.00          0
        । (label_id: 10)                                         0.00       0.00       0.00          0
        ؟ (label_id: 11)                                         0.00       0.00       0.00          0
        ، (label_id: 12)                                         0.00       0.00       0.00          0
        ; (label_id: 13)                                         0.00       0.00       0.00          0
        ። (label_id: 14)                                        91.27      97.90      94.47      25341
        ፣ (label_id: 15)                                        61.93      82.11      70.60       5818
        ፧ (label_id: 16)                                        67.41      81.73      73.89       1177
        -------------------
        micro avg                                               99.08      99.08      99.08     762000
        macro avg                                               80.11      90.26      84.63     762000
        weighted avg                                            99.21      99.08      99.13     762000
    
    cap test report: 
        label                                                precision    recall       f1           support   
        LOWER (label_id: 0)                                     98.40      98.03      98.21       1064
        UPPER (label_id: 1)                                     71.23      75.36      73.24         69
        -------------------
        micro avg                                               96.65      96.65      96.65       1133
        macro avg                                               84.81      86.69      85.73       1133
        weighted avg                                            96.74      96.65      96.69       1133
    
    seg test report: 
        label                                                precision    recall       f1           support   
        NOSTOP (label_id: 0)                                    99.99      99.85      99.92     743158
        FULLSTOP (label_id: 1)                                  95.20      99.62      97.36      21842
        -------------------
        micro avg                                               99.85      99.85      99.85     765000
        macro avg                                               97.59      99.74      98.64     765000
        weighted avg                                            99.85      99.85      99.85     765000
    

    中文

    punct_post test report: 
        label                                                precision    recall       f1           support   
        <NULL> (label_id: 0)                                    99.53      97.31      98.41     435611
        <ACRONYM> (label_id: 1)                                  0.00       0.00       0.00          0
        . (label_id: 2)                                          0.00       0.00       0.00          0
        , (label_id: 3)                                          0.00       0.00       0.00          0
        ? (label_id: 4)                                          0.00       0.00       0.00          0
        ? (label_id: 5)                                         81.85      87.31      84.49       1513
        , (label_id: 6)                                         74.08      93.67      82.73      35921
        。 (label_id: 7)                                         96.51      96.93      96.72      32097
        、 (label_id: 8)                                          0.00       0.00       0.00          0
        ・ (label_id: 9)                                          0.00       0.00       0.00          0
        । (label_id: 10)                                         0.00       0.00       0.00          0
        ؟ (label_id: 11)                                         0.00       0.00       0.00          0
        ، (label_id: 12)                                         0.00       0.00       0.00          0
        ; (label_id: 13)                                         0.00       0.00       0.00          0
        ። (label_id: 14)                                         0.00       0.00       0.00          0
        ፣ (label_id: 15)                                         0.00       0.00       0.00          0
        ፧ (label_id: 16)                                         0.00       0.00       0.00          0
        -------------------
        micro avg                                               97.00      97.00      97.00     505142
        macro avg                                               87.99      93.81      90.59     505142
        weighted avg                                            97.48      97.00      97.15     505142
    
    cap test report: 
        label                                                precision    recall       f1           support   
        LOWER (label_id: 0)                                     94.89      94.98      94.94       2951
        UPPER (label_id: 1)                                     81.34      81.03      81.18        796
        -------------------
        micro avg                                               92.02      92.02      92.02       3747
        macro avg                                               88.11      88.01      88.06       3747
        weighted avg                                            92.01      92.02      92.01       3747
    
    seg test report: 
        label                                                precision    recall       f1           support   
        NOSTOP (label_id: 0)                                    99.99      99.97      99.98     473642
        FULLSTOP (label_id: 1)                                  99.55      99.90      99.72      34500
        -------------------
        micro avg                                               99.96      99.96      99.96     508142
        macro avg                                               99.77      99.93      99.85     508142
        weighted avg                                            99.96      99.96      99.96     508142
    

    日语

    punct_post test report: 
        label                                                precision    recall       f1           support   
        <NULL> (label_id: 0)                                    99.34      95.90      97.59     406341
        <ACRONYM> (label_id: 1)                                  0.00       0.00       0.00          0
        . (label_id: 2)                                          0.00       0.00       0.00          0
        , (label_id: 3)                                          0.00       0.00       0.00          0
        ? (label_id: 4)                                          0.00       0.00       0.00          0
        ? (label_id: 5)                                         70.55      73.56      72.02       1456
        , (label_id: 6)                                          0.00       0.00       0.00          0
        。 (label_id: 7)                                         94.38      96.95      95.65      32537
        、 (label_id: 8)                                         54.28      87.62      67.03      18610
        ・ (label_id: 9)                                         28.18      71.64      40.45       1100
        । (label_id: 10)                                         0.00       0.00       0.00          0
        ؟ (label_id: 11)                                         0.00       0.00       0.00          0
        ، (label_id: 12)                                         0.00       0.00       0.00          0
        ; (label_id: 13)                                         0.00       0.00       0.00          0
        ። (label_id: 14)                                         0.00       0.00       0.00          0
        ፣ (label_id: 15)                                         0.00       0.00       0.00          0
        ፧ (label_id: 16)                                         0.00       0.00       0.00          0
        -------------------
        micro avg                                               95.51      95.51      95.51     460044
        macro avg                                               69.35      85.13      74.55     460044
        weighted avg                                            96.91      95.51      96.00     460044
    
    cap test report: 
        label                                                precision    recall       f1           support   
        LOWER (label_id: 0)                                     92.33      94.03      93.18       4174
        UPPER (label_id: 1)                                     83.51      79.46      81.43       1587
        -------------------
        micro avg                                               90.02      90.02      90.02       5761
        macro avg                                               87.92      86.75      87.30       5761
        weighted avg                                            89.90      90.02      89.94       5761
    
    seg test report: 
        label                                                precision    recall       f1           support   
        NOSTOP (label_id: 0)                                    99.99      99.92      99.96     428544
        FULLSTOP (label_id: 1)                                  99.07      99.87      99.47      34500
        -------------------
        micro avg                                               99.92      99.92      99.92     463044
        macro avg                                               99.53      99.90      99.71     463044
        weighted avg                                            99.92      99.92      99.92     463044
    

    印地语

    punct_post test report: 
        label                                                precision    recall       f1           support   
        <NULL> (label_id: 0)                                    99.75      99.44      99.59     560358
        <ACRONYM> (label_id: 1)                                  0.00       0.00       0.00          0
        . (label_id: 2)                                          0.00       0.00       0.00          0
        , (label_id: 3)                                         69.55      78.48      73.75       8084
        ? (label_id: 4)                                         63.30      87.07      73.31        317
        ? (label_id: 5)                                          0.00       0.00       0.00          0
        , (label_id: 6)                                          0.00       0.00       0.00          0
        。 (label_id: 7)                                          0.00       0.00       0.00          0
        、 (label_id: 8)                                          0.00       0.00       0.00          0
        ・ (label_id: 9)                                          0.00       0.00       0.00          0
        । (label_id: 10)                                        96.92      98.66      97.78      32118
        ؟ (label_id: 11)                                         0.00       0.00       0.00          0
        ، (label_id: 12)                                         0.00       0.00       0.00          0
        ; (label_id: 13)                                         0.00       0.00       0.00          0
        ። (label_id: 14)                                         0.00       0.00       0.00          0
        ፣ (label_id: 15)                                         0.00       0.00       0.00          0
        ፧ (label_id: 16)                                         0.00       0.00       0.00          0
        -------------------
        micro avg                                               99.11      99.11      99.11     600877
        macro avg                                               82.38      90.91      86.11     600877
        weighted avg                                            99.17      99.11      99.13     600877
    
    cap test report: 
        label                                                precision    recall       f1           support   
        LOWER (label_id: 0)                                     97.19      96.72      96.95       2466
        UPPER (label_id: 1)                                     89.14      90.60      89.86        734
        -------------------
        micro avg                                               95.31      95.31      95.31       3200
        macro avg                                               93.17      93.66      93.41       3200
        weighted avg                                            95.34      95.31      95.33       3200
    
    seg test report: 
        label                                                precision    recall       f1           support   
        NOSTOP (label_id: 0)                                   100.00      99.99      99.99     569472
        FULLSTOP (label_id: 1)                                  99.82      99.99      99.91      34405
        -------------------
        micro avg                                               99.99      99.99      99.99     603877
        macro avg                                               99.91      99.99      99.95     603877
        weighted avg                                            99.99      99.99      99.99     603877
    

    阿拉伯语

    punct_post test report: 
        label                                                precision    recall       f1           support   
        <NULL> (label_id: 0)                                    99.30      96.94      98.10     688043
        <ACRONYM> (label_id: 1)                                 93.33      77.78      84.85         18
        . (label_id: 2)                                         93.31      93.78      93.54      28175
        , (label_id: 3)                                          0.00       0.00       0.00          0
        ? (label_id: 4)                                          0.00       0.00       0.00          0
        ? (label_id: 5)                                          0.00       0.00       0.00          0
        , (label_id: 6)                                          0.00       0.00       0.00          0
        。 (label_id: 7)                                          0.00       0.00       0.00          0
        、 (label_id: 8)                                          0.00       0.00       0.00          0
        ・ (label_id: 9)                                          0.00       0.00       0.00          0
        । (label_id: 10)                                         0.00       0.00       0.00          0
        ؟ (label_id: 11)                                        65.93      82.79      73.40        860
        ، (label_id: 12)                                        44.89      79.20      57.30      20941
        ; (label_id: 13)                                         0.00       0.00       0.00          0
        ። (label_id: 14)                                         0.00       0.00       0.00          0
        ፣ (label_id: 15)                                         0.00       0.00       0.00          0
        ፧ (label_id: 16)                                         0.00       0.00       0.00          0
        -------------------
        micro avg                                               96.29      96.29      96.29     738037
        macro avg                                               79.35      86.10      81.44     738037
        weighted avg                                            97.49      96.29      96.74     738037
    
    cap test report: 
        label                                                precision    recall       f1           support   
        LOWER (label_id: 0)                                     97.10      99.49      98.28       4137
        UPPER (label_id: 1)                                     98.71      92.89      95.71       1729
        -------------------
        micro avg                                               97.55      97.55      97.55       5866
        macro avg                                               97.90      96.19      96.99       5866
        weighted avg                                            97.57      97.55      97.52       5866
    
    seg test report: 
        label                                                precision    recall       f1           support   
        NOSTOP (label_id: 0)                                    99.99      99.97      99.98     710456
        FULLSTOP (label_id: 1)                                  99.39      99.85      99.62      30581
        -------------------
        micro avg                                               99.97      99.97      99.97     741037
        macro avg                                               99.69      99.91      99.80     741037
        weighted avg                                            99.97      99.97      99.97     741037
    

    附加内容

    缩略词、缩写和双大写单词

    本节简要展示了模型处理以下情况的行为:

  • 缩略词:"NATO"
  • 假缩略词:在 "NATO" 的位置上使用 "NHTG"
  • 可能是缩略词或专有名词的二义性术语:"Tuny"
  • 双大写单词:"McDavid"
  • 首字母缩略词: "p.m."
  • 输入缩略词等
    from typing import List
    
    from punctuators.models import PunctCapSegModelONNX
    
    m: PunctCapSegModelONNX = PunctCapSegModelONNX.from_pretrained(
        "1-800-BAD-CODE/xlm-roberta_punctuation_fullstop_truecase"
    )
    
    input_texts = [
        "the us is a nato member as a nato member the country enjoys security guarantees notably article 5",
        "the us is a nhtg member as a nhtg member the country enjoys security guarantees notably article 5",
        "the us is a tuny member as a tuny member the country enjoys security guarantees notably article 5",
        "connor andrew mcdavid is a canadian professional ice hockey centre and captain of the edmonton oilers of the national hockey league the oilers selected him first overall in the 2015 nhl entry draft mcdavid spent his childhood playing ice hockey against older children",
        "please rsvp for the party asap preferably before 8 pm tonight",
    ]
    
    results: List[List[str]] = m.infer(
        texts=input_texts, apply_sbd=True,
    )
    for input_text, output_texts in zip(input_texts, results):
        print(f"Input: {input_text}")
        print(f"Outputs:")
        for text in output_texts:
            print(f"\t{text}")
        print()
    
    预期输出
    Input: the us is a nato member as a nato member the country enjoys security guarantees notably article 5
    Outputs:
        The U.S. is a NATO member.
        As a NATO member, the country enjoys security guarantees, notably Article 5.
    
    Input: the us is a nhtg member as a nhtg member the country enjoys security guarantees notably article 5
    Outputs:
        The U.S. is a NHTG member.
        As a NHTG member, the country enjoys security guarantees, notably Article 5.
    
    Input: the us is a tuny member as a tuny member the country enjoys security guarantees notably article 5
    Outputs:
        The U.S. is a Tuny member.
        As a Tuny member, the country enjoys security guarantees, notably Article 5.
    
    Input: connor andrew mcdavid is a canadian professional ice hockey centre and captain of the edmonton oilers of the national hockey league the oilers selected him first overall in the 2015 nhl entry draft mcdavid spent his childhood playing ice hockey against older children
    Outputs:
        Connor Andrew McDavid is a Canadian professional ice hockey centre and captain of the Edmonton Oilers of the National Hockey League.
        The Oilers selected him first overall in the 2015 NHL entry draft.
        McDavid spent his childhood playing ice hockey against older children.
    
    Input: please rsvp for the party asap preferably before 8 pm tonight
    Outputs:
        Please RSVP for the party ASAP, preferably before 8 p.m. tonight.