README.md

---
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