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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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# 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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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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## 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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## 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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This model was trained on news data, and may not perform well on conversational or informal data. |
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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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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 |
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context of a 47-language model, Spanish questions were over-sampled by selecting more of these sentences from |
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additional training data that was not used. However, this seems to have "over-corrected" the problem and a lot |
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of Spanish question marks are predicted. This can be fixed by exposing prior probabilities, but I'll fine-tune |
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it later to fix this the right way. |
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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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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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## 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 pretty 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) 99.18 98.47 98.82 538769 |
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<ACRONYM> (label_id: 1) 66.03 78.63 71.78 571 |
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. (label_id: 2) 90.66 93.68 92.14 30581 |
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, (label_id: 3) 74.18 82.93 78.31 23230 |
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? (label_id: 4) 78.10 80.08 79.07 1024 |
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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 97.56 97.56 97.56 594175 |
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macro avg 81.63 86.76 84.03 594175 |
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weighted avg 97.70 97.56 97.62 594175 |
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``` |
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```text |
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cap test report: |
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label precision recall f1 support |
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LOWER (label_id: 0) 99.71 99.85 99.78 2036824 |
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UPPER (label_id: 1) 96.40 93.27 94.81 87747 |
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------------------- |
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micro avg 99.58 99.58 99.58 2124571 |
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macro avg 98.06 96.56 97.30 2124571 |
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weighted avg 99.57 99.58 99.58 2124571 |
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``` |
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```text |
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seg test report: |
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label precision recall f1 support |
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NOSTOP (label_id: 0) 99.97 99.98 99.98 564228 |
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FULLSTOP (label_id: 1) 99.73 99.54 99.64 32947 |
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------------------- |
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micro avg 99.96 99.96 99.96 597175 |
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macro avg 99.85 99.76 99.81 597175 |
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weighted avg 99.96 99.96 99.96 597175 |
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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) 99.96 99.76 99.86 609200 |
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¿ (label_id: 1) 39.66 77.89 52.56 1221 |
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------------------- |
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micro avg 99.72 99.72 99.72 610421 |
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macro avg 69.81 88.82 76.21 610421 |
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weighted avg 99.83 99.72 99.76 610421 |
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``` |
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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) 99.17 98.44 98.80 553100 |
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<ACRONYM> (label_id: 1) 23.33 43.75 30.43 48 |
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. (label_id: 2) 91.92 92.58 92.25 29623 |
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, (label_id: 3) 73.07 82.04 77.30 26432 |
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? (label_id: 4) 49.44 71.84 58.57 1218 |
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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 97.39 97.39 97.39 610421 |
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macro avg 67.39 77.73 71.47 610421 |
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weighted avg 97.58 97.39 97.47 610421 |
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``` |
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```text |
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cap test report: |
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label precision recall f1 support |
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LOWER (label_id: 0) 99.82 99.86 99.84 2222062 |
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UPPER (label_id: 1) 95.96 94.64 95.29 75940 |
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------------------- |
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micro avg 99.69 99.69 99.69 2298002 |
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macro avg 97.89 97.25 97.57 2298002 |
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weighted avg 99.69 99.69 99.69 2298002 |
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``` |
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```text |
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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 99.97 99.98 580519 |
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FULLSTOP (label_id: 1) 99.52 99.81 99.66 32902 |
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------------------- |
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micro avg 99.96 99.96 99.96 613421 |
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macro avg 99.75 99.89 99.82 613421 |
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weighted avg 99.96 99.96 99.96 613421 |
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``` |
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</details> |
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