finetuned__roberta-clinical-wl-es__augmented-ultrasounds-ner

This model is a fine-tuned version of manucos/finetuned__roberta-clinical-wl-es__augmented-ultrasounds on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3995
• Precision: 0.7932
• Recall: 0.8775
• F1: 0.8333
• Accuracy: 0.9231

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	22	1.2788	0.5687	0.2763	0.3719	0.6256
No log	2.0	44	0.6691	0.6975	0.7470	0.7214	0.8576
No log	3.0	66	0.4416	0.7649	0.8168	0.7900	0.9051
No log	4.0	88	0.3715	0.7350	0.8279	0.7787	0.9115
No log	5.0	110	0.3398	0.7658	0.8441	0.8031	0.9221
No log	6.0	132	0.3320	0.7808	0.8472	0.8126	0.9216
No log	7.0	154	0.3306	0.7844	0.8431	0.8127	0.9199
No log	8.0	176	0.3321	0.7778	0.8502	0.8124	0.9199
No log	9.0	198	0.3398	0.7845	0.8512	0.8165	0.9196
No log	10.0	220	0.3445	0.7731	0.8553	0.8121	0.9197
No log	11.0	242	0.3560	0.7804	0.8522	0.8147	0.9196
No log	12.0	264	0.3516	0.7904	0.8664	0.8267	0.9214
No log	13.0	286	0.3553	0.7923	0.8725	0.8304	0.9228
No log	14.0	308	0.3644	0.7896	0.8775	0.8313	0.9223
No log	15.0	330	0.3706	0.7927	0.8745	0.8316	0.9214
No log	16.0	352	0.3763	0.7921	0.8755	0.8317	0.9228
No log	17.0	374	0.3811	0.7869	0.8745	0.8284	0.9228
No log	18.0	396	0.3772	0.7830	0.8765	0.8271	0.9238
No log	19.0	418	0.3888	0.7829	0.8796	0.8284	0.9218
No log	20.0	440	0.3878	0.7900	0.8755	0.8305	0.9208
No log	21.0	462	0.3916	0.7853	0.8775	0.8289	0.9221
No log	22.0	484	0.3884	0.7938	0.8806	0.8349	0.9231
0.2377	23.0	506	0.3926	0.7921	0.8715	0.8299	0.9219
0.2377	24.0	528	0.3951	0.7956	0.8785	0.8350	0.9239
0.2377	25.0	550	0.3941	0.7920	0.8785	0.8330	0.9229
0.2377	26.0	572	0.3970	0.7934	0.8785	0.8338	0.9236
0.2377	27.0	594	0.3979	0.7965	0.8796	0.8360	0.9241
0.2377	28.0	616	0.3999	0.7949	0.8785	0.8346	0.9236
0.2377	29.0	638	0.4001	0.7925	0.8775	0.8329	0.9233
0.2377	30.0	660	0.3995	0.7932	0.8775	0.8333	0.9231

Framework versions

• Transformers 4.40.0
• Pytorch 2.2.1+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1