learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
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: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 120

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
3.5853	2.5641	200	3.5139	0.3019	0.2288	0.3019	0.2345
3.2713	5.1282	400	3.0864	0.5377	0.4640	0.5377	0.4615
2.7401	7.6923	600	2.5215	0.6302	0.5401	0.6302	0.5601
2.1343	10.2564	800	1.9296	0.7170	0.6254	0.7170	0.6571
1.5063	12.8205	1000	1.3920	0.7566	0.6875	0.7566	0.7114
1.0361	15.3846	1200	1.0702	0.8132	0.7713	0.8132	0.7810
0.769	17.9487	1400	0.9110	0.8189	0.7982	0.8189	0.7962
0.6071	20.5128	1600	0.8106	0.8302	0.8136	0.8302	0.8125
0.4613	23.0769	1800	0.7313	0.8491	0.8441	0.8491	0.8370
0.358	25.6410	2000	0.6852	0.8547	0.8498	0.8547	0.8452
0.284	28.2051	2200	0.6448	0.8679	0.8611	0.8679	0.8600
0.22	30.7692	2400	0.6581	0.8679	0.8781	0.8679	0.8660
0.1745	33.3333	2600	0.6068	0.8717	0.8729	0.8717	0.8666
0.1375	35.8974	2800	0.6068	0.8830	0.8946	0.8830	0.8827
0.1096	38.4615	3000	0.5848	0.8962	0.9096	0.8962	0.8961
0.0889	41.0256	3200	0.5778	0.8925	0.9057	0.8925	0.8922
0.0722	43.5897	3400	0.6095	0.8830	0.8983	0.8830	0.8835
0.0636	46.1538	3600	0.6074	0.8849	0.8986	0.8849	0.8849
0.0548	48.7179	3800	0.5972	0.8962	0.9094	0.8962	0.8962
0.0428	51.2821	4000	0.6226	0.8906	0.9069	0.8906	0.8928
0.0416	53.8462	4200	0.6335	0.8849	0.9036	0.8849	0.8861
0.0375	56.4103	4400	0.6533	0.8811	0.8930	0.8811	0.8795
0.037	58.9744	4600	0.6516	0.8849	0.8941	0.8849	0.8843
0.028	61.5385	4800	0.6452	0.8887	0.8993	0.8887	0.8893
0.0341	64.1026	5000	0.6523	0.8925	0.9056	0.8925	0.8938
0.0253	66.6667	5200	0.6546	0.8887	0.9020	0.8887	0.8902
0.024	69.2308	5400	0.7024	0.8830	0.8950	0.8830	0.8830
0.0244	71.7949	5600	0.6767	0.8925	0.9043	0.8925	0.8932
0.0201	74.3590	5800	0.6885	0.8887	0.9009	0.8887	0.8897
0.0258	76.9231	6000	0.6870	0.8925	0.9059	0.8925	0.8937
0.0227	79.4872	6200	0.7150	0.8868	0.9003	0.8868	0.8873
0.019	82.0513	6400	0.7534	0.8830	0.8964	0.8830	0.8834
0.0224	84.6154	6600	0.7238	0.8887	0.9002	0.8887	0.8882
0.0173	87.1795	6800	0.7238	0.8906	0.9043	0.8906	0.8911
0.0212	89.7436	7000	0.7031	0.8925	0.9064	0.8925	0.8934
0.0187	92.3077	7200	0.7364	0.8943	0.9088	0.8943	0.8956
0.0187	94.8718	7400	0.7152	0.8943	0.9110	0.8943	0.8961
0.0168	97.4359	7600	0.7188	0.8849	0.8980	0.8849	0.8856
0.0191	100.0	7800	0.7268	0.8887	0.9039	0.8887	0.8903
0.0167	102.5641	8000	0.7339	0.8906	0.9033	0.8906	0.8911
0.013	105.1282	8200	0.7268	0.8925	0.9065	0.8925	0.8936
0.0168	107.6923	8400	0.7346	0.8906	0.9038	0.8906	0.8909
0.0132	110.2564	8600	0.7319	0.8906	0.9038	0.8906	0.8909
0.0139	112.8205	8800	0.7405	0.8906	0.9024	0.8906	0.8908
0.0136	115.3846	9000	0.7361	0.8906	0.9024	0.8906	0.8908
0.0144	117.9487	9200	0.7377	0.8906	0.9037	0.8906	0.8906

Framework versions

Transformers 4.57.3
Pytorch 2.9.1+cu128
Datasets 4.4.1
Tokenizers 0.22.1

Tiime/camembert-pcg-real-transactions

camembert-pcg-real-transactions

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Tiime/
camembert-pcg-real-transactions