udayGay/resume_model

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

• Train Loss: 1.1040
• Validation Loss: 1.4298
• Train Accuracy: 0.6640
• Train Precision: 0.5589
• Train Recall: 0.5938
• Train F1: 0.5692
• Epoch: 29

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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1470, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Accuracy	Train Precision	Train Recall	Train F1	Epoch
3.1654	3.1440	0.0402	0.0017	0.0417	0.0033	0
3.1553	3.1474	0.0282	0.0012	0.0417	0.0023	1
3.1208	3.0528	0.0805	0.0147	0.0812	0.0225	2
2.9896	2.8784	0.1469	0.0746	0.1384	0.0825	3
2.6886	2.5739	0.3300	0.2207	0.3033	0.2182	4
2.2855	2.1620	0.4547	0.3432	0.4138	0.3395	5
1.9018	1.9030	0.5151	0.4141	0.4679	0.4118	6
1.6218	1.7029	0.5795	0.4872	0.5205	0.4854	7
1.4058	1.5916	0.6217	0.5261	0.5595	0.5278	8
1.2705	1.4954	0.6479	0.5457	0.5815	0.5557	9
1.1692	1.4469	0.6600	0.5548	0.5896	0.5643	10
1.1179	1.4298	0.6640	0.5589	0.5938	0.5692	11
1.1162	1.4298	0.6640	0.5589	0.5938	0.5692	12
1.1109	1.4298	0.6640	0.5589	0.5938	0.5692	13
1.1142	1.4298	0.6640	0.5589	0.5938	0.5692	14
1.1095	1.4298	0.6640	0.5589	0.5938	0.5692	15
1.1108	1.4298	0.6640	0.5589	0.5938	0.5692	16
1.1133	1.4298	0.6640	0.5589	0.5938	0.5692	17
1.1132	1.4298	0.6640	0.5589	0.5938	0.5692	18
1.1064	1.4298	0.6640	0.5589	0.5938	0.5692	19
1.1098	1.4298	0.6640	0.5589	0.5938	0.5692	20
1.1029	1.4298	0.6640	0.5589	0.5938	0.5692	21
1.1055	1.4298	0.6640	0.5589	0.5938	0.5692	22
1.1125	1.4298	0.6640	0.5589	0.5938	0.5692	23
1.1081	1.4298	0.6640	0.5589	0.5938	0.5692	24
1.1125	1.4298	0.6640	0.5589	0.5938	0.5692	25
1.1130	1.4298	0.6640	0.5589	0.5938	0.5692	26
1.1101	1.4298	0.6640	0.5589	0.5938	0.5692	27
1.1134	1.4298	0.6640	0.5589	0.5938	0.5692	28
1.1040	1.4298	0.6640	0.5589	0.5938	0.5692	29

Framework versions

• Transformers 4.31.0
• TensorFlow 2.12.0
• Datasets 2.14.4
• Tokenizers 0.13.3