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Open-weight model · Token classification

punctuate-all

by KREDOR kredor/punctuate-all

This is based on Oliver Guhr's work. The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on twelve languages instead of four.

Parameters—
Context514
Weights1.1 GB
Licensemit
AccessOpen weights
Monthly Downloads815k

Model Card

By KREDOR, published under mit, revision 0fe37019de3f.

This is based on Oliver Guhr's work. The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on twelve languages instead of four. The languages are: English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian. precision recall f1-score support accuracy 0.98 84425503 macro avg 0.83 0.74 0.77 84425503 weighted avg 0.98 0.98 0.98 84425503

Read KREDOR's full model card

This is based on Oliver Guhr's work. The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on twelve languages instead of four. The languages are: English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian.

----- report -----

          precision    recall  f1-score   support

       0       0.99      0.99      0.99  73317475
       .       0.94      0.95      0.95   4484845
       ,       0.86      0.86      0.86   6100650
       ?       0.88      0.85      0.86    136479
       -       0.60      0.29      0.39    233630
       :       0.71      0.49      0.58    152424

accuracy                           0.98  84425503

macro avg 0.83 0.74 0.77 84425503 weighted avg 0.98 0.98 0.98 84425503

----- confusion matrix -----

 t/p      0     .     ,     ?     -     : 
    0   1.0   0.0   0.0   0.0   0.0   0.0 
    .   0.0   1.0   0.0   0.0   0.0   0.0 
    ,   0.1   0.0   0.9   0.0   0.0   0.0 
    ?   0.0   0.1   0.0   0.8   0.0   0.0 
    -   0.1   0.1   0.5   0.0   0.3   0.0 
    :   0.0   0.3   0.1   0.0   0.0   0.5

Configuration

Architecture
XLMRobertaForTokenClassification
Context length (tokens)
514
Layers
12
Hidden size
768
Feed-forward size
3,072
Attention heads
12
Vocabulary size
250,002
Stored precision
float32
Model type
xlm-roberta

Identity and Version

Repository
kredor/punctuate-all
Publisher
KREDOR
Task
Token classification
Modality
Text
Library
transformers
Parameters
Not stated by the source
Languages
xlm-roberta
Revision
0fe37019de3f5e4fbd83289fd94e07fa588e47df
First published
2022-04-09
Last updated
2024-04-26

Files and Weights

8 files, 1.1 GB in total. The weights are 2 files totalling 1.1 GB in bin.

Weights2 files · 1.1 GB
Configuration2 files · 1.2 KB
Tokenizer2 files · 17.1 MB
Documentation1 file · 1.4 KB
Repository1 file · 1.3 KB
Every file
FileTypeSizeSHA-256
pytorch_model.binWeights1.1 GB 9aec7aa51b4f
training_args.binWeights3.2 KB 294e3f7a9228
config.jsonConfiguration914 B —
special_tokens_map.jsonConfiguration239 B —
README.mdDocumentation1.4 KB —
.gitattributesRepository1.3 KB —
tokenizer.jsonTokenizer17.1 MB f2c509a525eb
tokenizer_config.jsonTokenizer447 B —

License and Download

License
mit
Access
Open weights, no gate
Download size
1.1 GB
Download from KREDOR

Released by KREDOR through its official repository on Hugging Face. Read the license.

Built From

  • Trained on (disclosed) wmt/europarl

Memory Requirements

PrecisionWeights in memory
As published1.1 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About punctuate-all

Can I use punctuate-all commercially?

Yes. punctuate-all is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

What is punctuate-all's context length?

514 tokens, from the maximum position embeddings in its published configuration.

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