prueba

This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1440
• Precision: 0.6923
• Recall: 0.6096
• F1: 0.6483
• Accuracy: 0.9719

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: 2.5e-05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 32

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	29	0.3513	0.0	0.0	0.0	0.9259
No log	2.0	58	0.2696	0.0	0.0	0.0	0.9259
No log	3.0	87	0.2879	0.0	0.0	0.0	0.9259
No log	4.0	116	0.2318	0.0714	0.0080	0.0143	0.9361
No log	5.0	145	0.2055	0.2222	0.0558	0.0892	0.9376
No log	6.0	174	0.2076	0.3793	0.0876	0.1424	0.9464
No log	7.0	203	0.1630	0.4831	0.2271	0.3089	0.9525
No log	8.0	232	0.1529	0.5515	0.3625	0.4375	0.9573
No log	9.0	261	0.1519	0.5972	0.3426	0.4354	0.9603
No log	10.0	290	0.1399	0.6272	0.4223	0.5048	0.9639
No log	11.0	319	0.1412	0.6096	0.4542	0.5205	0.9641
No log	12.0	348	0.1320	0.5969	0.4661	0.5235	0.9646
No log	13.0	377	0.1311	0.6515	0.5139	0.5746	0.9671
No log	14.0	406	0.1300	0.6329	0.5219	0.5721	0.9656
No log	15.0	435	0.1346	0.6345	0.4980	0.5580	0.9672
No log	16.0	464	0.1361	0.6329	0.5219	0.5721	0.9669
No log	17.0	493	0.1312	0.6532	0.5777	0.6131	0.9689
0.1181	18.0	522	0.1327	0.6756	0.6056	0.6387	0.9694
0.1181	19.0	551	0.1495	0.7234	0.5418	0.6196	0.9704
0.1181	20.0	580	0.1328	0.6872	0.5777	0.6277	0.9707
0.1181	21.0	609	0.1363	0.6667	0.6215	0.6433	0.9710
0.1181	22.0	638	0.1392	0.6884	0.5896	0.6352	0.9712
0.1181	23.0	667	0.1377	0.6437	0.6335	0.6386	0.9704
0.1181	24.0	696	0.1434	0.6504	0.5857	0.6164	0.9697
0.1181	25.0	725	0.1418	0.6944	0.5976	0.6424	0.9710
0.1181	26.0	754	0.1426	0.6739	0.6175	0.6445	0.9715
0.1181	27.0	783	0.1447	0.7085	0.6295	0.6667	0.9734
0.1181	28.0	812	0.1432	0.6903	0.6215	0.6541	0.9727
0.1181	29.0	841	0.1421	0.7162	0.6335	0.6723	0.9729
0.1181	30.0	870	0.1431	0.6875	0.6135	0.6484	0.9720
0.1181	31.0	899	0.1431	0.6844	0.6135	0.6471	0.9717
0.1181	32.0	928	0.1440	0.6923	0.6096	0.6483	0.9719

Framework versions

• Transformers 4.27.3
• Pytorch 1.13.1+cu116
• Datasets 2.10.1
• Tokenizers 0.13.2