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': np.float32(2e-05), 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Validation Loss	Train Rougel	Epoch
3.2887	2.7414	tf.Tensor(0.10495151, shape=(), dtype=float32)	0
2.8740	2.6131	tf.Tensor(0.107647784, shape=(), dtype=float32)	1
2.7677	2.5511	tf.Tensor(0.10973339, shape=(), dtype=float32)	2
2.7164	2.5125	tf.Tensor(0.110031, shape=(), dtype=float32)	3
2.6642	2.4831	tf.Tensor(0.11158478, shape=(), dtype=float32)	4
2.6308	2.4624	tf.Tensor(0.113469236, shape=(), dtype=float32)	5
2.6021	2.4441	tf.Tensor(0.11510291, shape=(), dtype=float32)	6
2.5760	2.4320	tf.Tensor(0.116486646, shape=(), dtype=float32)	7
2.5621	2.4232	tf.Tensor(0.11774607, shape=(), dtype=float32)	8
2.5392	2.4123	tf.Tensor(0.11893746, shape=(), dtype=float32)	9
2.5222	2.4055	tf.Tensor(0.11986255, shape=(), dtype=float32)	10
2.4992	2.3962	tf.Tensor(0.120771945, shape=(), dtype=float32)	11
2.4807	2.3923	tf.Tensor(0.1215765, shape=(), dtype=float32)	12
2.4680	2.3856	tf.Tensor(0.12224119, shape=(), dtype=float32)	13
2.4490	2.3822	tf.Tensor(0.122846335, shape=(), dtype=float32)	14
2.4389	2.3774	tf.Tensor(0.12332024, shape=(), dtype=float32)	15
2.4202	2.3729	tf.Tensor(0.12359309, shape=(), dtype=float32)	16
2.4068	2.3701	tf.Tensor(0.12389527, shape=(), dtype=float32)	17
2.3999	2.3701	tf.Tensor(0.124175206, shape=(), dtype=float32)	18
2.3849	2.3652	tf.Tensor(0.12443648, shape=(), dtype=float32)	19
2.3710	2.3610	tf.Tensor(0.1246448, shape=(), dtype=float32)	20
2.3574	2.3599	tf.Tensor(0.124864124, shape=(), dtype=float32)	21
2.3420	2.3582	tf.Tensor(0.12505461, shape=(), dtype=float32)	22
2.3352	2.3586	tf.Tensor(0.12529129, shape=(), dtype=float32)	23
2.3220	2.3555	tf.Tensor(0.12545374, shape=(), dtype=float32)	24
2.3098	2.3534	tf.Tensor(0.12562212, shape=(), dtype=float32)	25
2.3056	2.3542	tf.Tensor(0.12579708, shape=(), dtype=float32)	26
2.2965	2.3491	tf.Tensor(0.12598586, shape=(), dtype=float32)	27
2.2817	2.3493	tf.Tensor(0.12615614, shape=(), dtype=float32)	28
2.2715	2.3535	tf.Tensor(0.1262996, shape=(), dtype=float32)	29
2.2616	2.3515	tf.Tensor(0.1264528, shape=(), dtype=float32)	30
2.2469	2.3476	tf.Tensor(0.12659346, shape=(), dtype=float32)	31
2.2395	2.3482	tf.Tensor(0.12672439, shape=(), dtype=float32)	32
2.2285	2.3511	tf.Tensor(0.12683806, shape=(), dtype=float32)	33
2.2131	2.3494	tf.Tensor(0.1269755, shape=(), dtype=float32)	34
2.2146	2.3508	tf.Tensor(0.1270953, shape=(), dtype=float32)	35
2.2085	2.3461	tf.Tensor(0.12720396, shape=(), dtype=float32)	36
2.1952	2.3482	tf.Tensor(0.12734987, shape=(), dtype=float32)	37
2.1867	2.3508	tf.Tensor(0.12747498, shape=(), dtype=float32)	38
2.1763	2.3475	tf.Tensor(0.12761238, shape=(), dtype=float32)	39
2.1722	2.3516	tf.Tensor(0.12775333, shape=(), dtype=float32)	40
2.1613	2.3499	tf.Tensor(0.12791796, shape=(), dtype=float32)	41
2.1523	2.3493	tf.Tensor(0.12806812, shape=(), dtype=float32)	42
2.1389	2.3484	tf.Tensor(0.12821344, shape=(), dtype=float32)	43
2.1362	2.3516	tf.Tensor(0.12831683, shape=(), dtype=float32)	44
2.1225	2.3559	tf.Tensor(0.12842092, shape=(), dtype=float32)	45
2.1132	2.3547	tf.Tensor(0.12856483, shape=(), dtype=float32)	46
2.1046	2.3521	tf.Tensor(0.12868021, shape=(), dtype=float32)	47
2.0976	2.3542	tf.Tensor(0.12876385, shape=(), dtype=float32)	48
2.0932	2.3563	tf.Tensor(0.12886706, shape=(), dtype=float32)	49

Framework versions

Transformers 4.51.3
TensorFlow 2.18.0
Datasets 3.5.0
Tokenizers 0.21.1

AbSadr/AbSadr-Finetuned-Transformer-E

AbSadr-Finetuned-Transformer-E

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

AbSadr/
AbSadr-Finetuned-Transformer-E