README.md

metadata

license: apache-2.0
base_model: bert-base-cased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: biobert-biocause-trainer-oversample
    results: []

biobert-biocause-trainer-oversample

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7149
• Accuracy: 0.8457
• F1: 0.6735
• Recall: 0.6226
• Precision: 0.7333

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: 5e-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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
0.5227	0.07	25	0.5550	0.7765	0.2320	0.1321	0.9545
0.6695	0.14	50	0.5736	0.7315	0.5640	0.6792	0.4821
0.5501	0.22	75	0.5333	0.7621	0.5595	0.5912	0.5311
0.5193	0.29	100	0.4489	0.8119	0.48	0.3396	0.8182
0.5462	0.36	125	0.3952	0.8392	0.6269	0.5283	0.7706
0.4863	0.43	150	0.4829	0.8232	0.6541	0.6541	0.6541
0.4607	0.5	175	0.4429	0.8360	0.5641	0.4151	0.88
0.4302	0.58	200	0.4701	0.8103	0.6529	0.6981	0.6133
0.3965	0.65	225	0.5427	0.8071	0.6685	0.7610	0.5961
0.3838	0.72	250	0.4431	0.8296	0.6624	0.6541	0.6710
0.4917	0.79	275	0.6932	0.7203	0.6027	0.8302	0.4731
0.3751	0.86	300	0.4731	0.7781	0.6330	0.7484	0.5484
0.3926	0.94	325	0.4237	0.8424	0.6975	0.7107	0.6848
0.3654	1.01	350	0.3528	0.8521	0.7032	0.6855	0.7219
0.2255	1.08	375	0.6046	0.8392	0.6835	0.6792	0.6879
0.4107	1.15	400	0.4417	0.8569	0.6716	0.5723	0.8125
0.3405	1.22	425	0.4378	0.8376	0.6667	0.6352	0.7014
0.2532	1.3	450	0.5072	0.8264	0.6824	0.7296	0.6409
0.2366	1.37	475	0.5545	0.8232	0.6667	0.6918	0.6433
0.2102	1.44	500	0.5370	0.8633	0.6996	0.6226	0.7984
0.1455	1.51	525	0.6646	0.8553	0.6980	0.6541	0.7482
0.2918	1.59	550	0.6595	0.8296	0.6826	0.7170	0.6514
0.2585	1.66	575	0.6265	0.8392	0.6753	0.6541	0.6980
0.3427	1.73	600	0.5371	0.8376	0.6892	0.7044	0.6747
0.1538	1.8	625	0.6054	0.8585	0.6788	0.5849	0.8087
0.2565	1.87	650	0.5814	0.8601	0.6926	0.6164	0.7903
0.255	1.95	675	0.5811	0.8489	0.6968	0.6792	0.7152
0.2814	2.02	700	0.5238	0.8489	0.6846	0.6415	0.7338
0.0351	2.09	725	0.6550	0.8505	0.7010	0.6855	0.7171
0.0849	2.16	750	0.7147	0.8473	0.6780	0.6289	0.7353
0.145	2.23	775	0.8233	0.8344	0.7014	0.7610	0.6505
0.0889	2.31	800	0.7376	0.8505	0.7103	0.7170	0.7037
0.0968	2.38	825	0.7388	0.8521	0.6783	0.6101	0.7638
0.1507	2.45	850	0.7317	0.8537	0.6762	0.5975	0.7787
0.134	2.52	875	0.7362	0.8392	0.6795	0.6667	0.6928
0.1088	2.59	900	0.6987	0.8457	0.68	0.6415	0.7234
0.0854	2.67	925	0.7236	0.8553	0.6897	0.6289	0.7634
0.136	2.74	950	0.7118	0.8473	0.6844	0.6478	0.7254
0.0571	2.81	975	0.7155	0.8473	0.6780	0.6289	0.7353
0.1579	2.88	1000	0.7195	0.8521	0.6913	0.6478	0.7410
0.1093	2.95	1025	0.7146	0.8473	0.6780	0.6289	0.7353

Framework versions

• Transformers 4.37.2
• Pytorch 2.3.1
• Datasets 2.19.1
• Tokenizers 0.15.1