fimu-docproc-research/roberta-large-finetuned

xmelus/invoices-roberta-large

This model is a fine-tuned version of xlm-roberta-large on dataset . It achieves the following results on the evaluation set:

• Train Loss: 2.2317
• Train Accuracy: 0.0883
• Validation Loss: 2.1699
• Validation Accuracy: 0.0899
• Finished epochs: 13

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 754, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: mixed_float16

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
11.4516	0.0165	4.5115	0.0501	0
3.6182	0.0628	2.8398	0.0752	1
2.2317	0.0883	2.1699	0.0899	2
1.9700	0.0942	2.5529	0.0831	3
1.9714	0.0941	2.4961	0.0843	4
1.9682	0.0940	2.5089	0.0839	5
1.9546	0.0944	2.5029	0.0841	6
1.9808	0.0939	2.5140	0.0838	7
1.9728	0.0937	2.5212	0.0833	8
1.9655	0.0941	2.5575	0.0838	9
1.9708	0.0935	2.5419	0.0833	10
1.9693	0.0940	2.5304	0.0836	11
1.9614	0.0941	2.5176	0.0835	12

Framework versions

• Transformers 4.21.2
• TensorFlow 2.8.2
• Datasets 2.4.0
• Tokenizers 0.12.1