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---
license: cc-by-sa-4.0
datasets:
- cjvt/cc_gigafida
language:
- sl
tags:
- word case classification
---
---
language:
- sl
license: cc-by-sa-4.0
---
# sloberta-word-case-classification-multilabel
SloBERTa model finetuned on the Gigafida dataset for word case classification.
The input to the model is expected to be **fully lowercased text**.
The model classifies whether the input words should stay lowercased, be uppercased, or be all-uppercased. In addition, it provides a constrained explanation for its case classification.
See usage example below for more details.
## Usage example
Imagine we have the following Slovenian text. Asterisked words have an incorrect word casing.
```
Linus *torvalds* je *Finski* programer, Poznan kot izumitelj operacijskega sistema Linux.
(EN: Linus Torvalds is a Finnish programer, known as the inventor of the Linux operating sistem)
```
The model expects an all-lowercased input, so we pass it the following text:
```
linus *torvalds* je finski programer, poznan kot izumitelj operacijskega sistema linux.
```
The model might return the following predictions (note: predictions chosen for demonstration/explanation, not reproducibility!):
```
Linus -> UPPER_ENTITY, UPPER_BEGIN
Torvalds -> UPPER_ENTITY
je -> LOWER_OTHER
finski -> LOWER_ADJ_SKI
programer -> LOWER_OTHER
, -> LOWER_OTHER
Poznan -> LOWER_HYPERCORRECTION
kot -> LOWER_OTHER
izumitelj -> LOWER_OTHER
operacijskega -> LOWER_OTHER
sistema -> LOWER_OTHER
linux -> UPPER_ENTITY
```
Then we would compare the (coarse) predictions (i.e., LOWER/UPPER/UPPER_ALLUC) with the initial casing and observe the following:
- `Torvalds` is originally lowercased, but the model corrects it to uppercase (because it is an entity),
- `finski` is originally uppercased, but the model corrects it to lowercase (because it is an adjective with suffix -ski),
- `poznan` is originally uppercased, but the model corrects it to lowercase (the model assumes that the user made the mistake due to hypercorrection, meaning they naïvely uppercased a word after a character that could be punctuation),
The other predictions agree with the word case in the initial text, so they are assumed to be correct.
## More details
More concretely, the model is a 12-class multi-label classifier with the following class indices and interpretations:
```
0: "LOWER_OTHER", # lowercased for an uncaptured reason
1: "LOWER_HYPERCORRECTION", # lowercase due to hypercorrection (e.g., user automatically uppercased a word after a "." despite it not being a punctuation mark - the word should instead be lowercased)
2: "LOWER_ADJ_SKI", # lowercased because the word is an adjective ending in suffix -ski
3: "LOWER_ENTITY_PART", # lowercased word that is part of an entity (e.g., "Novo **mesto**")
4: "UPPER_OTHER", # upercased for an uncaptured reason
5: "UPPER_BEGIN", # upercased because the word begins a sentence
6: "UPPER_ENTITY", # uppercased word that is part of an entity
7: "UPPER_DIRECT_SPEECH", # upercased word due to direct speech
8: "UPPER_ADJ_OTHER", # upercased adjective for an uncaptured reason (usually this is a possesive adjective)
9: "UPPER_ALLUC_OTHER", # all-uppercased for an uncaptured reason
10: "UPPER_ALLUC_BEGIN", # all-uppercased because the word begins a sentence
11: "UPPER_ALLUC_ENTITY" # all-uppercased because the word is part of an entity
```
As the model is trained for multi-label classification, a word can be assigned multiple labels whose probability is > T. Naïvely T=0.5 can be used, but it is slightly better to use label thresholds optimized on a small validation set -
they are noted in the file `label_thresholds.json` and below (along with the validation set F1 achieved with the best threshold).
```
LOWER_OTHER: T=0.4500 -> F1 = 0.9965
LOWER_HYPERCORRECTION: T=0.5800 -> F1 = 0.8555
LOWER_ADJ_SKI: T=0.4810 -> F1 = 0.9863
LOWER_ENTITY_PART: T=0.4330 -> F1 = 0.8024
UPPER_OTHER: T=0.4460 -> F1 = 0.7538
UPPER_BEGIN: T=0.4690 -> F1 = 0.9905
UPPER_ENTITY: T=0.5030 -> F1 = 0.9670
UPPER_DIRECT_SPEECH: T=0.4170 -> F1 = 0.9852
UPPER_ADJ_OTHER: T=0.5080 -> F1 = 0.9431
UPPER_ALLUC_OTHER: T=0.4850 -> F1 = 0.8463
UPPER_ALLUC_BEGIN: T=0.5170 -> F1 = 0.9798
UPPER_ALLUC_ENTITY: T=0.4490 -> F1 = 0.9391
```
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