scideberta-cs-tdm-pretrained-finetuned-ner

This model is a fine-tuned version of sohamtiwari3120/scideberta-cs-tdm-pretrained on the generator dataset. It achieves the following results on the evaluation set:

• Loss: 0.6836
• Overall Precision: 0.5912
• Overall Recall: 0.6850
• Overall F1: 0.6347
• Overall Accuracy: 0.9609
• Datasetname F1: 0.5882
• Hyperparametername F1: 0.6897
• Hyperparametervalue F1: 0.7619
• Methodname F1: 0.6525
• Metricname F1: 0.7500
• Metricvalue F1: 0.6452
• Taskname F1: 0.5370

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy	Datasetname F1	Hyperparametername F1	Hyperparametervalue F1	Methodname F1	Metricname F1	Metricvalue F1	Taskname F1
No log	1.0	132	0.3507	0.3972	0.6870	0.5034	0.9410	0.4370	0.5441	0.5814	0.6124	0.5604	0.6207	0.3724
No log	2.0	264	0.3079	0.4066	0.7520	0.5278	0.9430	0.4138	0.5380	0.6222	0.5895	0.625	0.7273	0.4340
No log	3.0	396	0.3740	0.5007	0.7195	0.5905	0.9535	0.4882	0.6777	0.7500	0.6254	0.6747	0.7097	0.4962
0.4014	4.0	528	0.4072	0.5161	0.7154	0.5997	0.9540	0.5167	0.6612	0.6374	0.6337	0.6753	0.6061	0.5341
0.4014	5.0	660	0.4088	0.5590	0.7317	0.6338	0.9582	0.5660	0.6667	0.7397	0.6250	0.7226	0.75	0.5794
0.4014	6.0	792	0.4810	0.5201	0.7093	0.6002	0.9550	0.4874	0.5970	0.6506	0.6207	0.6708	0.6250	0.5756
0.4014	7.0	924	0.5288	0.5403	0.6809	0.6025	0.9576	0.4915	0.6500	0.6133	0.6255	0.7006	0.7879	0.5389
0.0912	8.0	1056	0.5281	0.5468	0.6890	0.6097	0.9574	0.5370	0.7143	0.6866	0.5854	0.6939	0.7742	0.5491
0.0912	9.0	1188	0.4744	0.5371	0.7358	0.6209	0.9560	0.5370	0.6341	0.6753	0.6554	0.6795	0.7059	0.5699
0.0912	10.0	1320	0.5498	0.5686	0.7073	0.6304	0.9586	0.5370	0.6349	0.7500	0.6553	0.7152	0.7742	0.5573
0.0912	11.0	1452	0.6424	0.5857	0.7012	0.6383	0.9597	0.56	0.6789	0.7246	0.6667	0.6974	0.6875	0.5757
0.0354	12.0	1584	0.5867	0.5641	0.6890	0.6203	0.9585	0.5185	0.6496	0.7213	0.6619	0.7152	0.7333	0.5402
0.0354	13.0	1716	0.5500	0.5667	0.6992	0.6260	0.9592	0.5524	0.6829	0.7222	0.6621	0.6466	0.7333	0.5607
0.0354	14.0	1848	0.5743	0.5780	0.7154	0.6394	0.9596	0.5283	0.6833	0.7222	0.6644	0.6716	0.7742	0.5960
0.0354	15.0	1980	0.6836	0.5912	0.6850	0.6347	0.9609	0.5882	0.6897	0.7619	0.6525	0.7500	0.6452	0.5370

Framework versions

• Transformers 4.23.1
• Pytorch 1.12.1+cu102
• Datasets 2.6.1
• Tokenizers 0.13.1