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---
license: lgpl-3.0
base_model: sdadas/polish-roberta-base-v2
tags:
- generated_from_trainer
datasets:
- nkjp1m
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: polish-roberta-base-v2-cposes-tagging
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: nkjp1m
type: nkjp1m
config: nkjp1m
split: test
args: nkjp1m
metrics:
- name: Precision
type: precision
value: 0.9913009231909743
- name: Recall
type: recall
value: 0.9912435137138621
- name: F1
type: f1
value: 0.9912722176212015
- name: Accuracy
type: accuracy
value: 0.9889172310669364
widget:
- text: "Niosę dwa miedziane leje"
- text: "Ale dzisiaj leje"
language:
- pl
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# polish-roberta-base-v2-cposes-tagging
This model is a fine-tuned version of [sdadas/polish-roberta-base-v2](https://huggingface.co/sdadas/polish-roberta-base-v2) on the nkjp1m dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0458
- Precision: 0.9913
- Recall: 0.9912
- F1: 0.9913
- Accuracy: 0.9889
You can find the training notebook here: https://github.com/WikKam/roberta-pos-finetuning
## Usage
```
from transformers import pipeline
nlp = pipeline("token-classification", "wkaminski/polish-roberta-base-v2-cposes-tagging")
nlp("Ale dzisiaj leje")
```
## Model description
This model is a coarse-part-of-speech tagger for the Polish language based on sdadas/polish-roberta-base-v2.
It support 13 classes representing coarse part of speech):
```
{
0: 'A',
1: 'Adv',
2: 'Comp',
3: 'Conj',
4: 'Dig',
5: 'Interj',
6: 'N',
7: 'Num',
8: 'Part',
9: 'Prep',
10: 'Punct',
11: 'V',
12: 'X'
}
```
Tags meaning is the same as in nkjp1m dataset:
| Tag | Description in English | Description in Polish | Example in Polish |
|-------|----------------------------------|-----------------------------|---------------------------|
| A | Adjective | przymiotnik | szybki |
| Adv | Adverb | przysłówek | szybko |
| Comp | Comparative / Complementizer | stopień porównawczy / spójnik podrzędny | lepszy / że |
| Conj | Conjunction | spójnik | i |
| Dig | Digit | cyfra | 5, 3 |
| Interj| Interjection | wykrzyknik | och! |
| N | Noun | rzeczownik | dom |
| Num | Numeral | liczebnik | jeden |
| Part | Particle | partykuła | by |
| Prep | Preposition | przyimek | w |
| Punct | Punctuation | interpunkcja | ., !, ? |
| V | Verb | czasownik | biegać |
| X | Unknown / Other | niesklasyfikowane | xxx |
## Intended uses & limitations
Even though we have some nice tools for pos-tagging in polish (http://morfeusz.sgjp.pl/), I needed a pos tagger for polish that could be easily loaded inside the browser. Huggingface supports such functionality and that's why I created this model.
## Training and evaluation data
Model was trained on a half of test data of the nkjp1m dataset (~0.5 milion tokens).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0471 | 1.0 | 2155 | 0.0491 | 0.9896 | 0.9900 | 0.9898 | 0.9873 |
| 0.0291 | 2.0 | 4310 | 0.0467 | 0.9901 | 0.9905 | 0.9903 | 0.9884 |
| 0.0191 | 3.0 | 6465 | 0.0458 | 0.9913 | 0.9912 | 0.9913 | 0.9889 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0 |