metadata

language: pl
tags:
  - generated_from_trainer
  - text-generation
widget:
  - text: Bolesław Leśmian - polski poeta
datasets:
  - wikipedia
metrics:
  - accuracy
model-index:
  - name: gpt_neo_pl_125M
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: wikipedia 20220720.pl
          type: wikipedia
          args: 20220720.pl
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.4312838299951148

gpt_neo_pl_125M_v2

This model was trained from scratch on the wikipedia 20220720.pl dataset. It achieves the following results on the evaluation set:

Loss: 3.3862
Accuracy: 0.4313

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
5.9469	0.02	1000	6.5843	0.1435
4.9953	0.05	2000	5.7709	0.1911
4.3754	0.07	3000	5.2624	0.2331
3.9795	0.1	4000	4.8752	0.2731
3.7099	0.12	5000	4.5927	0.3039
3.4747	0.15	6000	4.3942	0.3230
3.343	0.17	7000	4.2879	0.3349
3.2767	0.2	8000	4.1698	0.3459
3.1852	0.22	9000	4.0925	0.3534
3.0871	0.25	10000	4.0239	0.3608
3.0746	0.27	11000	3.9646	0.3664
2.9473	0.3	12000	3.9245	0.3706
2.9737	0.32	13000	3.8742	0.3754
2.9193	0.35	14000	3.8285	0.3796
2.8833	0.37	15000	3.7952	0.3837
2.8533	0.4	16000	3.7616	0.3873
2.8654	0.42	17000	3.7296	0.3907
2.8196	0.44	18000	3.7049	0.3936
2.7883	0.47	19000	3.6786	0.3966
2.747	0.49	20000	3.6488	0.3990
2.7355	0.52	21000	3.6243	0.4021
2.7355	0.54	22000	3.5982	0.4053
2.6999	0.57	23000	3.5765	0.4075
2.7243	0.59	24000	3.5558	0.4101
2.6526	0.62	25000	3.5371	0.4125
2.641	0.64	26000	3.5150	0.4146
2.6602	0.67	27000	3.4971	0.4168
2.644	0.69	28000	3.4812	0.4192
2.6558	0.72	29000	3.4622	0.4215
2.5664	0.74	30000	3.4504	0.4229
2.5669	0.77	31000	3.4376	0.4245
2.5498	0.79	32000	3.4263	0.4263
2.5874	0.82	33000	3.4169	0.4274
2.5555	0.84	34000	3.4067	0.4286
2.5502	0.86	35000	3.3997	0.4298
2.5232	0.89	36000	3.3946	0.4302
2.5369	0.91	37000	3.3898	0.4309
2.5335	0.94	38000	3.3869	0.4313
2.6032	0.96	39000	3.3853	0.4315
2.5244	0.99	40000	3.3850	0.4314

Framework versions

Transformers 4.22.0.dev0
Pytorch 1.12.0
Datasets 2.4.0
Tokenizers 0.12.1