license: apache-2.0
language:
- es
- ca
- fr
- pt
- it
- ro
library_name: generic
tags:
- text2text-generation
- punctuation
- fullstop
- truecase
- capitalization
widget:
- text: hola amigo cómo estás es un día lluvioso hoy
- text: >-
este modelo fue entrenado en un gpu a100 en realidad no se que dice esta
frase lo traduje con nmt
Model
This model restores punctuation, predicts full stops (sentence boundaries), and predicts true-casing (capitalization) for text in the 6 most popular Romance languages:
- Spanish
- French
- Portuguese
- Catalan
- Italian
- Romanian
Together, these languages cover approximately 97% of native speakers of the Romance language family.
The model comprises a SentencePiece tokenizer, a Transformer encoder, and MLP prediction heads.
This model predicts the following punctuation per input subtoken:
- .
- ,
- ?
- ¿
- ACRONYM
Though rare in these languages (relative to English), the special token ACRONYM allows fully punctuating tokens such as "pm" → "p.m.".
Widget notes If you use the widget, it'll take a minute to load the model since a "generic" library is used. Further, the widget does not respect multi-line output, so fullstop predictions are annotated with "\n".
Usage
The model is released as a SentencePiece tokenizer and an ONNX graph.
The easy way to use this model is to install punctuators:
pip install punctuators
If this package is broken, please let me know in the community tab (I update it for each model and break it a lot!).
Example Usage
from typing import List
from punctuators.models import PunctCapSegModelONNX
# Instantiate this model
# This will download the ONNX and SPE models. To clean up, delete this model from your HF cache directory.
m = PunctCapSegModelONNX.from_pretrained("pcs_romance")
# Define some input texts to punctuate, at least one per language
input_texts: List[str] = [
"este modelo fue entrenado en un gpu a100 en realidad no se que dice esta frase lo traduje con nmt",
"hola amigo cómo estás es un día lluvioso hoy",
"hola amic com va avui ha estat un dia plujós el català prediu massa puntuació per com s'ha entrenat",
"ciao amico come va oggi è stata una giornata piovosa",
"olá amigo como tá indo estava chuvoso hoje",
"salut l'ami comment ça va il pleuvait aujourd'hui",
"salut prietene cum stă treaba azi a fost ploios",
]
results: List[List[str]] = m.infer(input_texts)
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()
Exact output may vary based on the model version; here is the current output:
Expected Output
Input: este modelo fue entrenado en un gpu a100 en realidad no se que dice esta frase lo traduje con nmt
Outputs:
Este modelo fue entrenado en un GPU A100.
En realidad, no se que dice esta frase lo traduje con NMT.
Input: hola amigo cómo estás es un día lluvioso hoy
Outputs:
Hola, amigo.
¿Cómo estás?
Es un día lluvioso hoy.
Input: hola amic com va avui ha estat un dia plujós el català prediu massa puntuació per com s'ha entrenat
Outputs:
Hola, amic.
Com va avui?
Ha estat un dia plujós.
El català prediu massa puntuació per com s'ha entrenat.
Input: ciao amico come va oggi è stata una giornata piovosa
Outputs:
Ciao amico, come va?
Oggi è stata una giornata piovosa.
Input: olá amigo como tá indo estava chuvoso hoje
Outputs:
Olá, amigo, como tá indo?
Estava chuvoso hoje.
Input: salut l'ami comment ça va il pleuvait aujourd'hui
Outputs:
Salut l'ami.
Comment ça va?
Il pleuvait aujourd'hui.
Input: salut prietene cum stă treaba azi a fost ploios
Outputs:
Salut prietene, cum stă treaba azi?
A fost ploios.
If you prefer your output to not be broken into separate sentences, you can disable sentence boundary detection in the API call:
input_texts: List[str] = [
"hola amigo cómo estás es un día lluvioso hoy",
]
results: List[str] = m.infer(input_texts, apply_sbd=False)
print(results[0])
Instead of a List[List[str]] (a list of output sentences for each input), we get a List[str] (one output
sentence per input):
Hola, amigo. ¿Cómo estás? Es un día lluvioso hoy.
Training Data
For all languages except Catalan, this model was trained with ~10M lines of text per language from StatMT's News Crawl.
Catalan is not included in StatMT's News Crawl.
For completeness of the Romance language family, ~500k lines of OpenSubtitles was used for Catalan.
Due to this, Catalan performance may be sub-par and may over-predict punctuation and sentence breaks, which is typical of OpenSubtitles.
Training Parameters
This model was trained by concatenating between 1 and 14 random sentences. The concatenation points became sentence boundary targets, text was lower-cased to produce true-case targets, and punctuation was removed to create punctuation targets.
Batches were built by randomly sampling from each language. Each example is language homogenous (i.e., we only concatenate sentences from the same language). Batches were multilingual. Neither language tags nor language-specific paths are utilized in the graph.
The maximum length during training was 256 subtokens.
The punctuators package can punctuate inputs of any length.
This is accomplished behind the scenes by splitting the input into overlapping subsegments of 256 tokens, and combining the results.
If you use the raw ONNX graph, note that while the model will accept sequences up to 512 tokens, only 256 positional embeddings have been trained.
Contact
Contact me at [email protected] with requests or issues, or just let me know on the community tab.
Metrics
Test sets were generated with 3,000 lines of held-out data per language (OpenSubtitles for Catalan, News Crawl for all others). Examples were derived by concatenating 10 sentences per example, removing all punctuation, and lower-casing all letters.
Since punctuation is subjective (e.g., see "hello friend how's it going" in the above examples) punctuation metrics can be misleading.
Also, keep in mind that the data is noisy. Catalan is especially noisy, since it's OpenSubtitles (note how Catalan has a 50 instances of "¿" which should not appear).
Note that we call the label "¿" "pre-punctuation" since it is unique in that it appears before words, and thus we predict it separate from the other punctuation tokens.
Generally, periods are easy, commas are a harder, question marks are hard, and acronyms are rare and noisy.
Expand any of the following tabs to see metrics for that language.
Spanish metrics
Pre-punctuation report:
label precision recall f1 support
<NULL> (label_id: 0) 99.92 99.97 99.95 572069
¿ (label_id: 1) 81.93 60.46 69.57 1095
-------------------
micro avg 99.90 99.90 99.90 573164
macro avg 90.93 80.22 84.76 573164
weighted avg 99.89 99.90 99.89 573164
Punctuation report:
label precision recall f1 support
<NULL> (label_id: 0) 98.70 98.44 98.57 517310
<ACRONYM> (label_id: 1) 39.68 86.21 54.35 58
. (label_id: 2) 87.72 90.41 89.04 29267
, (label_id: 3) 73.17 74.68 73.92 25422
? (label_id: 4) 69.49 59.26 63.97 1107
-------------------
micro avg 96.90 96.90 96.90 573164
macro avg 73.75 81.80 75.97 573164
weighted avg 96.94 96.90 96.92 573164
True-casing report:
label precision recall f1 support
LOWER (label_id: 0) 99.85 99.73 99.79 2164982
UPPER (label_id: 1) 92.01 95.32 93.64 69437
-------------------
micro avg 99.60 99.60 99.60 2234419
macro avg 95.93 97.53 96.71 2234419
weighted avg 99.61 99.60 99.60 2234419
Fullstop report:
label precision recall f1 support
NOSTOP (label_id: 0) 100.00 99.98 99.99 543228
FULLSTOP (label_id: 1) 99.66 99.93 99.80 32931
-------------------
micro avg 99.98 99.98 99.98 576159
macro avg 99.83 99.96 99.89 576159
weighted avg 99.98 99.98 99.98 576159
Portuguese metrics
Pre-punctuation report:
label precision recall f1 support
<NULL> (label_id: 0) 100.00 100.00 100.00 539822
¿ (label_id: 1) 0.00 0.00 0.00 0
-------------------
micro avg 100.00 100.00 100.00 539822
macro avg 100.00 100.00 100.00 539822
weighted avg 100.00 100.00 100.00 539822
Punctuation report:
label precision recall f1 support
<NULL> (label_id: 0) 98.77 98.27 98.52 481148
<ACRONYM> (label_id: 1) 0.00 0.00 0.00 0
. (label_id: 2) 87.63 90.63 89.11 29090
, (label_id: 3) 74.44 78.69 76.50 28549
? (label_id: 4) 66.30 52.27 58.45 1035
-------------------
micro avg 96.74 96.74 96.74 539822
macro avg 81.79 79.96 80.65 539822
weighted avg 96.82 96.74 96.77 539822
True-casing report:
label precision recall f1 support
LOWER (label_id: 0) 99.90 99.82 99.86 2082598
UPPER (label_id: 1) 94.75 97.08 95.90 70555
-------------------
micro avg 99.73 99.73 99.73 2153153
macro avg 97.32 98.45 97.88 2153153
weighted avg 99.73 99.73 99.73 2153153
Fullstop report:
label precision recall f1 support
NOSTOP (label_id: 0) 100.00 99.98 99.99 509905
FULLSTOP (label_id: 1) 99.72 99.98 99.85 32909
-------------------
micro avg 99.98 99.98 99.98 542814
macro avg 99.86 99.98 99.92 542814
weighted avg 99.98 99.98 99.98 542814
Romanian metrics
Pre-punctuation report:
label precision recall f1 support
<NULL> (label_id: 0) 100.00 100.00 100.00 580702
¿ (label_id: 1) 0.00 0.00 0.00 0
-------------------
micro avg 100.00 100.00 100.00 580702
macro avg 100.00 100.00 100.00 580702
weighted avg 100.00 100.00 100.00 580702
Punctuation report:
label precision recall f1 support
<NULL> (label_id: 0) 98.56 98.47 98.51 520647
<ACRONYM> (label_id: 1) 52.00 79.89 63.00 179
. (label_id: 2) 87.29 89.37 88.32 29852
, (label_id: 3) 75.26 74.69 74.97 29218
? (label_id: 4) 60.73 55.46 57.98 806
-------------------
micro avg 96.74 96.74 96.74 580702
macro avg 74.77 79.57 76.56 580702
weighted avg 96.74 96.74 96.74 580702
Truecasing report:
label precision recall f1 support
LOWER (label_id: 0) 99.84 99.75 99.79 2047297
UPPER (label_id: 1) 93.56 95.65 94.59 77424
-------------------
micro avg 99.60 99.60 99.60 2124721
macro avg 96.70 97.70 97.19 2124721
weighted avg 99.61 99.60 99.60 2124721
Fullstop report:
label precision recall f1 support
NOSTOP (label_id: 0) 100.00 99.96 99.98 550858
FULLSTOP (label_id: 1) 99.26 99.94 99.60 32833
-------------------
micro avg 99.95 99.95 99.95 583691
macro avg 99.63 99.95 99.79 583691
weighted avg 99.96 99.95 99.96 583691
Italian metrics
Pre-punctuation report:
label precision recall f1 support
<NULL> (label_id: 0) 100.00 100.00 100.00 577636
¿ (label_id: 1) 0.00 0.00 0.00 0
-------------------
micro avg 100.00 100.00 100.00 577636
macro avg 100.00 100.00 100.00 577636
weighted avg 100.00 100.00 100.00 577636
Punctuation report:
label precision recall f1 support
<NULL> (label_id: 0) 98.10 97.73 97.91 522727
<ACRONYM> (label_id: 1) 41.76 48.72 44.97 78
. (label_id: 2) 81.71 86.70 84.13 28881
, (label_id: 3) 61.72 63.24 62.47 24703
? (label_id: 4) 62.55 41.78 50.10 1247
-------------------
micro avg 95.58 95.58 95.58 577636
macro avg 69.17 67.63 67.92 577636
weighted avg 95.64 95.58 95.60 577636
Truecasing report:
label precision recall f1 support
LOWER (label_id: 0) 99.76 99.70 99.73 2160781
UPPER (label_id: 1) 91.18 92.76 91.96 72471
-------------------
micro avg 99.47 99.47 99.47 2233252
macro avg 95.47 96.23 95.85 2233252
weighted avg 99.48 99.47 99.48 2233252
Fullstop report:
label precision recall f1 support
NOSTOP (label_id: 0) 99.99 99.98 99.99 547875
FULLSTOP (label_id: 1) 99.72 99.91 99.82 32742
-------------------
micro avg 99.98 99.98 99.98 580617
macro avg 99.86 99.95 99.90 580617
weighted avg 99.98 99.98 99.98 580617
French metrics
Pre-punctuation report:
label precision recall f1 support
<NULL> (label_id: 0) 100.00 100.00 100.00 614010
¿ (label_id: 1) 0.00 0.00 0.00 0
-------------------
micro avg 100.00 100.00 100.00 614010
macro avg 100.00 100.00 100.00 614010
weighted avg 100.00 100.00 100.00 614010
Punctuation report:
label precision recall f1 support
<NULL> (label_id: 0) 98.72 98.57 98.65 556366
<ACRONYM> (label_id: 1) 38.46 71.43 50.00 49
. (label_id: 2) 86.41 88.56 87.47 28969
, (label_id: 3) 72.15 72.80 72.47 27183
? (label_id: 4) 75.81 67.78 71.57 1443
-------------------
micro avg 96.88 96.88 96.88 614010
macro avg 74.31 79.83 76.03 614010
weighted avg 96.91 96.88 96.89 614010
Truecasing report:
label precision recall f1 support
LOWER (label_id: 0) 99.84 99.80 99.82 2127174
UPPER (label_id: 1) 93.72 94.73 94.22 66496
-------------------
micro avg 99.65 99.65 99.65 2193670
macro avg 96.78 97.27 97.02 2193670
weighted avg 99.65 99.65 99.65 2193670
Fullstop report:
label precision recall f1 support
NOSTOP (label_id: 0) 99.99 99.94 99.97 584331
FULLSTOP (label_id: 1) 98.92 99.90 99.41 32661
-------------------
micro avg 99.94 99.94 99.94 616992
macro avg 99.46 99.92 99.69 616992
weighted avg 99.94 99.94 99.94 616992
Catalan metrics
Pre-punctuation report:
label precision recall f1 support
<NULL> (label_id: 0) 99.97 100.00 99.98 143817
¿ (label_id: 1) 0.00 0.00 0.00 50
-------------------
micro avg 99.97 99.97 99.97 143867
macro avg 49.98 50.00 49.99 143867
weighted avg 99.93 99.97 99.95 143867
Punctuation report:
label precision recall f1 support
<NULL> (label_id: 0) 97.61 97.73 97.67 119040
<ACRONYM> (label_id: 1) 0.00 0.00 0.00 28
. (label_id: 2) 74.02 79.46 76.65 15282
, (label_id: 3) 60.88 50.75 55.36 5836
? (label_id: 4) 64.94 60.28 62.52 3681
-------------------
micro avg 92.90 92.90 92.90 143867
macro avg 59.49 57.64 58.44 143867
weighted avg 92.76 92.90 92.80 143867
Truecasing report:
label precision recall f1 support
LOWER (label_id: 0) 99.81 99.83 99.82 422395
UPPER (label_id: 1) 97.09 96.81 96.95 24854
-------------------
micro avg 99.66 99.66 99.66 447249
macro avg 98.45 98.32 98.39 447249
weighted avg 99.66 99.66 99.66 447249
Fullstop report:
label precision recall f1 support
NOSTOP (label_id: 0) 99.93 99.63 99.78 123867
FULLSTOP (label_id: 1) 97.97 99.59 98.77 22000
-------------------
micro avg 99.63 99.63 99.63 145867
macro avg 98.95 99.61 99.28 145867
weighted avg 99.63 99.63 99.63 145867