Xlm Roberta Punctuation Fullstop Truecase

🚀 XLM-RoBERTa Punctuation and True-casing Model

This is an xlm-roberta fine-tuned model that can restore punctuation, true-case (capitalize), and detect sentence boundaries in 47 languages.

🚀 Quick Start

If you want to just play with the model, the widget on this page will suffice. To use the model offline, the following snippets show how to use the model both with a wrapper (available from PyPI) and manual usage (using the ONNX and SentencePiece models in this repo).

✨ Features

• Multilingual Support: This model supports 47 languages, including Afrikaans, Arabic, English, Spanish, Chinese, and many others.
• Punctuation Restoration: It can restore punctuation marks such as commas, periods, and question marks to text.
• True-casing: The model can correctly capitalize words based on their context.
• Sentence Boundary Detection: It can detect the boundaries between sentences.

📦 Installation

The easiest way to use this model is to install punctuators:

$ pip install punctuators

💻 Usage Examples

Basic Usage

Usage via punctuators package

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()

Expected output

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.

Advanced Usage

Manual Usage

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}")

Expected output

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.

📚 Documentation

Model Architecture

This model implements the following graph, which allows punctuation, true-casing, and fullstop prediction in every language without language-specific behavior:

Tokenizer

# 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'

from sentencepiece import S

📄 License

This project is licensed under the Apache 2.0 license.