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--- |
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license: apache-2.0 |
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library_name: generic |
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tags: |
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- text2text-generation |
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- punctuation |
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- sentence-boundary-detection |
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- truecasing |
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- true-casing |
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language: |
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- af |
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- am |
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- ar |
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- bg |
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- bn |
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- de |
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- el |
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- en |
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- es |
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- et |
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- fa |
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- fi |
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- fr |
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- gu |
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- hi |
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- hr |
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- hu |
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- id |
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- is |
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- it |
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- ja |
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- kk |
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- kn |
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- ko |
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- ky |
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- lt |
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- lv |
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- mk |
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- ml |
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- mr |
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- nl |
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- or |
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- pa |
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- pl |
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- ps |
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- pt |
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- ro |
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- ru |
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- rw |
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- so |
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- sr |
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- sw |
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- ta |
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- te |
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- tr |
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- uk |
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- zh |
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--- |
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# Model Overview |
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This is an `xlm-roberta` fine-tuned to restore punctuation, true-case (capitalize), |
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and detect sentence boundaries (full stops) in 47 languages. |
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# Usage |
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The easiest way to use this model is to install [punctuators](https://github.com/1-800-BAD-CODE/punctuators): |
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```bash |
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$ pip install punctuators |
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``` |
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Though this is just an ONNX and SentencePiece model, so you may run it as you wish. |
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The input to the `punctuators` API is a list (batch) of strings. |
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Each string will be punctuated, true-cased, and segmented on predicted full stops. |
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The output will therefore be a list of list of strings: one list of segmented sentences per input text. |
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To disable full stops, use `m.infer(texts, apply_sbd=False)`. |
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The output will then be a list of strings: one punctuated, true-cased string per input text. |
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<details open> |
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<summary>Example Usage</summary> |
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```python |
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from typing import List |
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from punctuators.models import PunctCapSegModelONNX |
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m: PunctCapSegModelONNX = PunctCapSegModelONNX.from_pretrained( |
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"1-800-BAD-CODE/xlm-roberta_punctuation_fullstop_truecase" |
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) |
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input_texts: List[str] = [ |
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"hola mundo cómo estás estamos bajo el sol y hace mucho calor santa coloma abre los huertos urbanos a las escuelas de la ciudad", |
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"hello friend how's it going it's snowing outside right now in connecticut a large storm is moving in", |
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"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", |
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"በባለፈው ሳምንት ኢትዮጵያ ከሶማሊያ 3 ሺህ ወታደሮቿንም እንዳስወጣች የሶማሊያው ዳልሳን ሬድዮ ዘግቦ ነበር ጸጥታ ሃይሉና ህዝቡ ተቀናጅቶ በመስራቱ በመዲናዋ ላይ የታቀደው የጥፋት ሴራ ከሽፏል", |
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"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", |
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"hallo freund wie geht's es war heute ein regnerischer tag nicht wahr ich verbrachte den tag drinnen um trocken zu bleiben", |
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"हैलो दोस्त ये कैसा चल रहा है आज बारिश का दिन था न मैंने सूखा रहने के लिए दिन घर के अंदर बिताया", |
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"كيف تجري الامور كان يومًا ممطرًا اليوم أليس كذلك قضيت اليوم في الداخل لأظل جافًا", |
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] |
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results: List[List[str]] = m.infer( |
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texts=input_texts, apply_sbd=True, |
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) |
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for input_text, output_texts in zip(input_texts, results): |
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print(f"Input: {input_text}") |
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print(f"Outputs:") |
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for text in output_texts: |
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print(f"\t{text}") |
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print() |
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``` |
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</details> |
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<details open> |
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<summary>Expected output</summary> |
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```text |
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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 |
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Outputs: |
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Hola mundo, ¿cómo estás? |
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Estamos bajo el sol y hace mucho calor. |
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Santa Coloma abre los huertos urbanos a las escuelas de la ciudad. |
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Input: hello friend how's it going it's snowing outside right now in connecticut a large storm is moving in |
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Outputs: |
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Hello friend, how's it going? |
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It's snowing outside right now. |
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In Connecticut, a large storm is moving in. |
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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 |
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Outputs: |
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未來，疫苗將有望覆蓋3歲以上全年齡段。 |
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美國與北約軍隊已全部撤離。 |
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還有，鐵路，公路在內的各項基建的來源都將枯竭。 |
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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 |
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Outputs: |
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በባለፈው ሳምንት ኢትዮጵያ ከሶማሊያ 3 ሺህ ወታደሮቿንም እንዳስወጣች የሶማሊያው ዳልሳን ሬድዮ ዘግቦ ነበር። |
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ጸጥታ ሃይሉና ህዝቡ ተቀናጅቶ በመስራቱ በመዲናዋ ላይ የታቀደው የጥፋት ሴራ ከሽፏል። |
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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 |
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Outputs: |
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こんにちは、友人、調子はどう？ |
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今日は雨の日でしたね。 |
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乾いた状態を保つために、一日中、室内で過ごしました。 |
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Input: hallo freund wie geht's es war heute ein regnerischer tag nicht wahr ich verbrachte den tag drinnen um trocken zu bleiben |
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Outputs: |
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Hallo Freund, wie geht's? |
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Es war heute ein regnerischer Tag, nicht wahr? |
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Ich verbrachte den Tag drinnen, um trocken zu bleiben. |
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Input: हैलो दोस्त ये कैसा चल रहा है आज बारिश का दिन था न मैंने सूखा रहने के लिए दिन घर के अंदर बिताया |
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Outputs: |
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हैलो दोस्त, ये कैसा चल रहा है? |
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आज बारिश का दिन था न, मैंने सूखा रहने के लिए दिन घर के अंदर बिताया। |
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Input: كيف تجري الامور كان يومًا ممطرًا اليوم أليس كذلك قضيت اليوم في الداخل لأظل جافًا |
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Outputs: |
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كيف تجري الامور؟ |
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كان يومًا ممطرًا اليوم، أليس كذلك؟ |
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قضيت اليوم في الداخل لأظل جافًا. |
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``` |
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</details> |
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# Model Architecture |
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This model implements the following graph, which allows punctuation, true-casing, and fullstop prediction |
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in every language without language-specific behavior: |
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 |
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We start by tokenizing the text and encoding it with XLM-Roberta, which is the pre-trained portion of this graph. |
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Then we predict punctuation before and after every subtoken. |
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Predicting before each token allows for Spanish inverted question marks. |
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Predicting after every token allows for all other punctuation, including punctuation within continuous-script |
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languages and acronyms. |
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We use embeddings to represent the predicted punctuation tokens to inform the sentence boundary head of the |
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punctuation that'll be inserted into the text. This allows proper full stop prediction, since certain punctuation |
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tokens (periods, questions marks, etc.) are strongly correlated with sentence boundaries. |
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We then shift full stop predictions to the right by one, to inform the true-casing head of where the beginning |
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of each new sentence is. This is important since true-casing is strongly correlated to sentence boundaries. |
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For true-casing, we predict `N` predictions per subtoken, where `N` is the number of characters in the subtoken. |
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In practice, `N` is the maximum subtoken length and extra predictions are ignored. Essentially, true-casing is |
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modeled as a multi-label problem. This allows for upper-casing arbitrary characters, e.g., "NATO", "MacDonald", "mRNA", etc. |
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Applying all these predictions to the input text, we can punctuate, true-case, and split sentences in any language. |
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## Tokenizer |
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Instead of the hacky wrapper used by FairSeq and strangely ported (not fixed) by HuggingFace, the `xlm-roberta` SentencePiece model was adjusted to correctly encode |
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the text. Per HF's comments, |
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```python |
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# Original fairseq vocab and spm vocab must be "aligned": |
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# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
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# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- |
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# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' |
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# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' |
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``` |
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The SP model was un-hacked with the following snippet |
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(SentencePiece experts, let me know if there is a problem here): |
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```python |
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from sentencepiece import SentencePieceProcessor |
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from sentencepiece.sentencepiece_model_pb2 import ModelProto |
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m = ModelProto() |
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m.ParseFromString(open("/path/to/xlmroberta/sentencepiece.bpe.model", "rb").read()) |
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pieces = list(m.pieces) |
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pieces = ( |
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[ |
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ModelProto.SentencePiece(piece="<s>", type=ModelProto.SentencePiece.Type.CONTROL), |
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ModelProto.SentencePiece(piece="<pad>", type=ModelProto.SentencePiece.Type.CONTROL), |
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ModelProto.SentencePiece(piece="</s>", type=ModelProto.SentencePiece.Type.CONTROL), |
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ModelProto.SentencePiece(piece="<unk>", type=ModelProto.SentencePiece.Type.UNKNOWN), |
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] |
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+ pieces[3:] |
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+ [ModelProto.SentencePiece(piece="<mask>", type=ModelProto.SentencePiece.Type.USER_DEFINED)] |
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) |
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del m.pieces[:] |
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m.pieces.extend(pieces) |
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with open("/path/to/new/sp.model", "wb") as f: |
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f.write(m.SerializeToString()) |
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``` |
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Now we can use just the SP model without a wrapper. |
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## Post-Punctuation Tokens |
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This model predicts the following set of punctuation tokens after each subtoken: |
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| Token | Description | Relevant Languages | |
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| ---: | :---------- | :----------- | |
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| \<NULL\> | No punctuation | All | |
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| \<ACRONYM\> | Every character in this subword is followed by a period | Primarily English, some European | |
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| . | Latin full stop | Many | |
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| , | Latin comma | Many | |
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| ? | Latin question mark | Many | |
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| ？ | Full-width question mark | Chinese, Japanese | |
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| ， | Full-width comma | Chinese, Japanese | |
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| 。 | Full-width full stop | Chinese, Japanese | |
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| 、 | Ideographic comma | Chinese, Japanese | |
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| ・ | Middle dot | Japanese | |
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| । | Danda | Hindi, Bengali, Oriya | |
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| ؟ | Arabic question mark | Arabic | |
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| ; | Greek question mark | Greek | |
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| ። | Ethiopic full stop | Amharic | |
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| ፣ | Ethiopic comma | Amharic | |
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| ፧ | Ethiopic question mark | Amharic | |
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## Pre-Punctuation Tokens |
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This model predicts the following set of punctuation tokens before each subword: |
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| Token | Description | Relevant Languages | |
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| ---: | :---------- | :----------- | |
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| \<NULL\> | No punctuation | All | |
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| ¿ | Inverted question mark | Spanish | |
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# Training Details |
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This model was trained in the NeMo framework. |
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This model was trained on an A100 for approximately 9 hours. |
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## Training Data |
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This model was trained with News Crawl data from WMT. |
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1M lines of text for each language was used, except for a few low-resource languages which may have used less. |
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Languages were chosen based on whether the News Crawl corpus contained enough reliable-quality data as judged by the author. |
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# Limitations |
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## Sentence Boundaries / Fullstops |
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Fullstop (sentence boundary) detection is near-perfect with news data, but misses obvious sentence boundaries |
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when several short sentences appear contiguously. |
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With News crawl, SBD F1 is > 99.5%. With OpenSubtitles, SBD F1 drops unacceptably to < 90%. |
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When I figure out why this is, I'll fine-tune the SBD head. It's likely due to pre-processing and domain mis-match. |
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## Domain |
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This model was trained on news data, and may not perform well on conversational or informal data. Notably, |
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when presented with many short sentences, the model misses obvious sentence boundaries since the model was |
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trained on relatively-long sentences. |
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## Quality |
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Further, this model is unlikely to be of production quality. |
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It was trained with "only" 1M lines per language, and the dev sets may have been noisy due to the nature of web-scraped news data. |
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## Excessive Predictions |
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This model over-predicts Spanish question marks, especially the inverted question mark `¿` (see metrics below). |
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Since `¿` is a rare token, especially in the context of a 47-language model, Spanish questions were over-sampled |
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by selecting more of these sentences from additional training data that was not used. However, this seems to have |
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"over-corrected" the problem and a lot of Spanish question marks are predicted. |
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The model may also over-predict commas. |
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# Evaluation |
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In these metrics, keep in mind that |
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1. The data is noisy |
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2. Sentence boundaries and true-casing are conditioned on predicted punctuation, which is the most difficult task and sometimes incorrect. |
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When conditioning on reference punctuation, true-casing and SBD is practically 100% for most languages. |
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4. Punctuation can be subjective. E.g., |
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`Hola mundo, ¿cómo estás?` |
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or |
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`Hola mundo. ¿Cómo estás?` |
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When the sentences are longer and more practical, these ambiguities abound and affect all 3 analytics. |
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## Test Data and Example Generation |
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Each test example was generated using the following procedure: |
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1. Concatenate 11 random sentences (1 + 10 for each sentence in the test set) |
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2. Lower-case the concatenated sentence |
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3. Remove all punctuation |
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Targets are generated as we lower-case letters and remove punctuation. |
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The data is a held-out portion of News Crawl, which has been deduplicated. |
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3,000 lines of data per language was used, generating 3,000 unique examples of 11 sentences each. |
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We generate 3,000 examples, where example `i` begins with sentence `i` and is followed by 10 random |
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sentences selected from the 3,000 sentence test set. |
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For measuring true-casing and sentence boundary detection, reference punctuation tokens were used for |
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conditioning (see graph above). If we use predicted punctuation instead, then incorrect punctuation will |
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result in true-casing and SBD targets not aligning correctly and these metrics will be artificially low. |
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## Selected Language Evaluation Reports |
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For now, metrics for a few selected languages are shown below. |
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Given the amount of work required to collect and pretty-print metrics in 47 languages, I'll add more eventually. |
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Expand any of the following tabs to see metrics for that language. |
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<details> |
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<summary>English</summary> |
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```text |
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punct_post test report: |
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label precision recall f1 support |
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<NULL> (label_id: 0) 99.25 98.43 98.84 564908 |
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<ACRONYM> (label_id: 1) 63.14 84.67 72.33 613 |
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. (label_id: 2) 90.97 93.91 92.42 32040 |
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, (label_id: 3) 73.95 84.32 78.79 24271 |
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? (label_id: 4) 79.05 81.94 80.47 1041 |
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？ (label_id: 5) 0.00 0.00 0.00 0 |
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， (label_id: 6) 0.00 0.00 0.00 0 |
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。 (label_id: 7) 0.00 0.00 0.00 0 |
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、 (label_id: 8) 0.00 0.00 0.00 0 |
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・ (label_id: 9) 0.00 0.00 0.00 0 |
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। (label_id: 10) 0.00 0.00 0.00 0 |
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؟ (label_id: 11) 0.00 0.00 0.00 0 |
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، (label_id: 12) 0.00 0.00 0.00 0 |
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; (label_id: 13) 0.00 0.00 0.00 0 |
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። (label_id: 14) 0.00 0.00 0.00 0 |
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፣ (label_id: 15) 0.00 0.00 0.00 0 |
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፧ (label_id: 16) 0.00 0.00 0.00 0 |
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------------------- |
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micro avg 97.60 97.60 97.60 622873 |
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macro avg 81.27 88.65 84.57 622873 |
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weighted avg 97.77 97.60 97.67 622873 |
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``` |
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``` |
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cap test report: |
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label precision recall f1 support |
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LOWER (label_id: 0) 99.72 99.85 99.78 2134956 |
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UPPER (label_id: 1) 96.33 93.52 94.91 91996 |
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------------------- |
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micro avg 99.59 99.59 99.59 2226952 |
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macro avg 98.03 96.68 97.34 2226952 |
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weighted avg 99.58 99.59 99.58 2226952 |
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``` |
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|
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``` |
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seg test report: |
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label precision recall f1 support |
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NOSTOP (label_id: 0) 99.99 99.98 99.99 591540 |
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FULLSTOP (label_id: 1) 99.61 99.89 99.75 34333 |
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------------------- |
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micro avg 99.97 99.97 99.97 625873 |
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macro avg 99.80 99.93 99.87 625873 |
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weighted avg 99.97 99.97 99.97 625873 |
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``` |
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</details> |
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<details> |
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<summary>Spanish</summary> |
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```text |
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punct_pre test report: |
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label precision recall f1 support |
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<NULL> (label_id: 0) 99.94 99.89 99.92 636941 |
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¿ (label_id: 1) 56.73 71.35 63.20 1288 |
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------------------- |
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micro avg 99.83 99.83 99.83 638229 |
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macro avg 78.34 85.62 81.56 638229 |
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weighted avg 99.85 99.83 99.84 638229 |
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``` |
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``` |
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punct_post test report: |
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label precision recall f1 support |
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<NULL> (label_id: 0) 99.19 98.41 98.80 578271 |
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<ACRONYM> (label_id: 1) 30.10 56.36 39.24 55 |
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. (label_id: 2) 91.92 93.12 92.52 30856 |
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, (label_id: 3) 72.98 82.44 77.42 27761 |
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? (label_id: 4) 52.77 71.85 60.85 1286 |
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？ (label_id: 5) 0.00 0.00 0.00 0 |
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， (label_id: 6) 0.00 0.00 0.00 0 |
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。 (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 |
|
``` |
|
|
|
</details> |
|
|
|
|
|
<details> |
|
<summary>Amharic</summary> |
|
|
|
```text |
|
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 |
|
``` |
|
|
|
</details> |
|
|
|
|
|
<details> |
|
<summary>Chinese</summary> |
|
|
|
```text |
|
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 |
|
``` |
|
|
|
</details> |
|
|
|
|
|
<details> |
|
<summary>Japanese</summary> |
|
|
|
```text |
|
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 |
|
``` |
|
|
|
</details> |
|
|
|
|
|
<details> |
|
<summary>Hindi</summary> |
|
|
|
```text |
|
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 |
|
``` |
|
|
|
</details> |
|
|
|
|
|
<details> |
|
<summary>Arabic</summary> |
|
|
|
```text |
|
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 |
|
``` |
|
|
|
</details> |
|
|
