metadata

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

pos-polish-gpt2-small

This model was trained from polish-gpt2-small on 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

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

Datacollator:

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