polish-roberta-base-v2-cposes-tagging

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polish-roberta-base-v2-cposes-tagging

This model is a fine-tuned version of 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:

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

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0