tiny-vanilla-target-conll2003

This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1431
• Precision: 0.7507
• Recall: 0.8177
• F1: 0.7828
• Accuracy: 0.9581

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:

• learning_rate: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• training_steps: 5000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.7673	1.14	500	0.4291	0.4793	0.5160	0.4970	0.8920
0.3746	2.28	1000	0.2869	0.5976	0.6572	0.6260	0.9256
0.2869	3.42	1500	0.2292	0.6411	0.7184	0.6776	0.9370
0.236	4.56	2000	0.1988	0.6805	0.7516	0.7143	0.9438
0.2026	5.69	2500	0.1772	0.7047	0.7718	0.7367	0.9482
0.1798	6.83	3000	0.1649	0.7179	0.7864	0.7506	0.9514
0.158	7.97	3500	0.1559	0.7256	0.7987	0.7604	0.9543
0.1415	9.11	4000	0.1500	0.7379	0.8034	0.7693	0.9563
0.127	10.25	4500	0.1462	0.7532	0.8134	0.7821	0.9573
0.1173	11.39	5000	0.1431	0.7507	0.8177	0.7828	0.9581

Framework versions

• Transformers 4.26.0.dev0
• Pytorch 1.13.0+cu116
• Datasets 2.8.1.dev0
• Tokenizers 0.13.2