learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Top3 Accuracy	Top5 Accuracy	Precision	Recall	F1 Weighted	F1 Macro
10.4836	0.2000	5685	5.0665	0.1127	0.2548	0.3753	0.0669	0.1127	0.0599	0.0173
8.4373	0.4000	11370	4.0622	0.3031	0.4722	0.5786	0.2701	0.3031	0.2376	0.0689
7.1056	0.5999	17055	3.5299	0.3624	0.5438	0.6517	0.3858	0.3624	0.3307	0.1013
6.4190	0.7999	22740	3.1918	0.3942	0.5826	0.6804	0.4443	0.3942	0.3790	0.1313
6.0150	0.9999	28425	2.9816	0.4022	0.6066	0.7010	0.4656	0.4022	0.3937	0.1473
5.5633	1.1999	34110	2.7845	0.4255	0.6289	0.7215	0.4763	0.4255	0.4210	0.1830
5.3629	1.3999	39795	2.6584	0.4417	0.6436	0.7366	0.4848	0.4417	0.4327	0.1989
5.1547	1.5998	45480	2.5266	0.4505	0.6605	0.7506	0.5006	0.4505	0.4441	0.2065
4.8821	1.7998	51165	2.4337	0.4646	0.6726	0.7618	0.5107	0.4646	0.4597	0.2182
4.8468	1.9998	56850	2.3580	0.4751	0.6825	0.7700	0.5255	0.4751	0.4705	0.2366
4.5083	2.1998	62535	2.3354	0.4872	0.6988	0.7844	0.5255	0.4872	0.4839	0.2488
4.3747	2.3998	68220	2.2397	0.4968	0.7037	0.7875	0.5416	0.4968	0.4955	0.2542
4.3334	2.5997	73905	2.2009	0.5124	0.7196	0.7999	0.5468	0.5124	0.5076	0.2679
4.3103	2.7997	79590	2.1654	0.5165	0.7254	0.8035	0.5578	0.5165	0.5118	0.2719
4.1306	2.9997	85275	2.1243	0.529	0.7327	0.8120	0.5584	0.529	0.5244	0.2871
3.8964	3.1997	90960	2.0769	0.5324	0.7378	0.8155	0.5664	0.5324	0.5295	0.3021
3.9158	3.3996	96645	2.0659	0.5306	0.7429	0.8186	0.5684	0.5306	0.5301	0.2978
3.7772	3.5996	102330	2.0397	0.5421	0.7443	0.8209	0.5725	0.5421	0.5381	0.3020
3.8012	3.7996	108015	2.0179	0.5422	0.7487	0.8231	0.5769	0.5422	0.5408	0.3028
3.7211	3.9996	113700	2.0004	0.5481	0.7525	0.8265	0.5791	0.5481	0.5448	0.3097
3.5257	4.1996	119385	1.9928	0.5482	0.7527	0.8268	0.5821	0.5482	0.5466	0.3095
3.5389	4.3995	125070	1.9911	0.5499	0.7541	0.8279	0.5807	0.5499	0.5470	0.3101
3.6145	4.5995	130755	1.9784	0.5515	0.7541	0.828	0.5811	0.5515	0.5488	0.3141
3.6517	4.7995	136440	1.9743	0.5529	0.7549	0.8287	0.5826	0.5529	0.5500	0.3170
3.5229	4.9995	142125	1.9741	0.5529	0.7549	0.8285	0.5827	0.5529	0.5500	0.3164

Framework versions

Transformers 5.2.0
Pytorch 2.10.0+cu128
Datasets 4.5.0
Tokenizers 0.22.2

Tiime/camembert-pcg-annotation-full

camembert-pcg-annotation-full

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Tiime/
camembert-pcg-annotation-full