tmvar_0.0001_ES12

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0194
• Precision: 0.8877
• Recall: 0.8973
• F1: 0.8925
• Accuracy: 0.9968

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: 0.0001
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2263	1.47	25	0.0788	0.0	0.0	0.0	0.9843
0.0492	2.94	50	0.0355	0.2576	0.3676	0.3029	0.9863
0.0258	4.41	75	0.0224	0.6	0.6811	0.6380	0.9933
0.013	5.88	100	0.0141	0.8267	0.9027	0.8630	0.9969
0.0031	7.35	125	0.0162	0.8218	0.8973	0.8579	0.9971
0.0028	8.82	150	0.0187	0.8449	0.8541	0.8495	0.9961
0.0024	10.29	175	0.0154	0.8267	0.9027	0.8630	0.9965
0.0014	11.76	200	0.0159	0.8221	0.9243	0.8702	0.9966
0.0013	13.24	225	0.0179	0.8579	0.8811	0.8693	0.9971
0.0009	14.71	250	0.0165	0.8807	0.8378	0.8587	0.9964
0.0005	16.18	275	0.0184	0.8549	0.8919	0.8730	0.9966
0.0003	17.65	300	0.0188	0.8777	0.8919	0.8847	0.9967
0.0002	19.12	325	0.0195	0.8474	0.8703	0.8587	0.9964
0.0002	20.59	350	0.0192	0.8836	0.9027	0.8930	0.9969
0.0003	22.06	375	0.0191	0.8889	0.9081	0.8984	0.9969
0.0002	23.53	400	0.0194	0.8877	0.8973	0.8925	0.9968

Framework versions

• Transformers 4.27.4
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.2