reward-opi-reddit-epochs-30

This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.1744
• Train Accuracy: 0.9468
• Validation Loss: 2.5324
• Validation Accuracy: 0.8363
• Epoch: 28

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:

• optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
0.0198	0.9936	4.0681	0.7263	0
0.0601	0.9828	2.5460	0.7581	1
0.1162	0.9635	3.9408	0.7648	2
0.0620	0.9811	3.7922	0.7527	3
0.0766	0.9810	3.7076	0.7856	4
0.0645	0.9888	2.6677	0.7954	5
0.1202	0.9677	2.4262	0.8147	6
0.1637	0.9480	3.3629	0.8363	7
0.1879	0.9501	2.1865	0.8363	8
0.1374	0.9583	2.5066	0.8363	9
0.0441	0.9914	2.7318	0.8363	10
0.1414	0.9592	2.8204	0.8363	11
0.1353	0.9667	2.3668	0.8363	12
0.1693	0.9433	2.6449	0.8363	13
0.2153	0.9341	2.1587	0.8363	14
0.2412	0.9241	2.1209	0.8363	15
0.2403	0.9219	2.7722	0.8363	16
0.1412	0.9589	2.9998	0.8363	17
0.0833	0.9798	2.6485	0.8363	18
0.1425	0.9629	2.3664	0.8363	19
0.2067	0.9393	2.2547	0.8363	20
0.2217	0.9281	2.5801	0.8363	21
0.0543	0.9891	1.1412	0.8363	22
0.0661	0.9875	2.6814	0.8363	23
0.1116	0.9775	2.5560	0.8363	24
0.0904	0.9795	2.5723	0.8363	25
0.1348	0.9667	2.4338	0.8363	26
0.2205	0.9343	2.2334	0.8363	27
0.1744	0.9468	2.5324	0.8363	28

Framework versions

• Transformers 4.36.1
• TensorFlow 2.15.0
• Datasets 2.18.0
• Tokenizers 0.15.2