---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pos-polish-gpt2-small
  results: []
license: mit
datasets:
- clarin-pl/nkjp-pos
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. -->

# pos-polish-gpt2-small

This model was trained from [polish-gpt2-small](https://huggingface.co/sdadas/polish-gpt2-small) on [clarin-pl/nkjp-pos](https://huggingface.co/datasets/clarin-pl/nkjp-pos) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3109
- Precision: 0.8793
- Recall: 0.9255
- F1: 0.9018
- Accuracy: 0.9371

## Model description

Trained from [polish-gpt2-small](https://huggingface.co/sdadas/polish-gpt2-small)

## Intended uses & limitations

Part-of-speech tagging for Polish language.
Tags description at the bottom of http://nkjp.pl/poliqarp/help/plse2.html

## Training and evaluation data

Dataset: [clarin-pl/nkjp-pos](https://huggingface.co/datasets/clarin-pl/nkjp-pos)

Datacollator:
```py
from transformers import DataCollatorForTokenClassification
data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
```

## Training procedure

GPU: RTX 3090

Training time: 00:50:24

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
|               | 0.0   |    0  | 3.6116          | 0.0464    | 0.0524 | 0.0492 | 0.0676   |
| 0.2303        | 1.0   | 1222  | 0.2159          | 0.8737    | 0.9225 | 0.8974 | 0.9347   |
| 0.1776        | 2.0   | 2444  | 0.2124          | 0.8799    | 0.9254 | 0.9021 | 0.9381   |
| 0.1467        | 3.0   | 3666  | 0.2205          | 0.8759    | 0.9241 | 0.8994 | 0.9368   |
| 0.1254        | 4.0   | 4889  | 0.2304          | 0.8792    | 0.9256 | 0.9018 | 0.9377   |
| 0.1091        | 5.0   | 6111  | 0.2480          | 0.8787    | 0.9251 | 0.9013 | 0.9375   |
| 0.0949        | 6.0   | 7333  | 0.2651          | 0.8794    | 0.9250 | 0.9016 | 0.9373   |
| 0.0857        | 7.0   | 8555  | 0.2794          | 0.8791    | 0.9251 | 0.9015 | 0.9372   |
| 0.079         | 8.0   | 9778  | 0.2922          | 0.8789    | 0.9247 | 0.9012 | 0.9366   |
| 0.0736        | 9.0   | 11000 | 0.3037          | 0.8807    | 0.9256 | 0.9026 | 0.9375   |
| 0.0691        | 10.0  | 12220 | 0.3109          | 0.8793    | 0.9255 | 0.9018 | 0.9371   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0