scideberta-cs-tdm-pretrained-finetuned-ner-finetuned-ner

This model is a fine-tuned version of bert-base-uncased on the generator dataset. It achieves the following results on the evaluation set:

• Loss: 0.7548
• Overall Precision: 0.5582
• Overall Recall: 0.7048
• Overall F1: 0.6230
• Overall Accuracy: 0.9578
• Datasetname F1: 0.6225
• Hyperparametername F1: 0.5707
• Hyperparametervalue F1: 0.6796
• Methodname F1: 0.6812
• Metricname F1: 0.5039
• Metricvalue F1: 0.7097
• Taskname F1: 0.5776

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	0.6819	0.2314	0.3769	0.2867	0.9125	0.1270	0.2305	0.2479	0.4072	0.3119	0.0635	0.2366
No log	2.0	264	0.4337	0.3977	0.5687	0.4681	0.9429	0.4516	0.3704	0.5419	0.5900	0.2446	0.4340	0.4609
No log	3.0	396	0.3968	0.3617	0.6367	0.4613	0.9335	0.4828	0.3586	0.5649	0.5331	0.3190	0.4800	0.4585
0.5603	4.0	528	0.3730	0.3605	0.6327	0.4593	0.9363	0.4750	0.3789	0.6066	0.5376	0.3229	0.4571	0.4375
0.5603	5.0	660	0.4132	0.4650	0.6871	0.5546	0.9482	0.4943	0.4965	0.6577	0.6465	0.4387	0.5306	0.5039
0.5603	6.0	792	0.4071	0.4482	0.6884	0.5429	0.9468	0.5541	0.4341	0.5991	0.6037	0.4865	0.64	0.5688
0.5603	7.0	924	0.4077	0.4830	0.6952	0.5700	0.9508	0.5063	0.4953	0.7032	0.6397	0.4286	0.6263	0.5469
0.1161	8.0	1056	0.5215	0.5426	0.6925	0.6085	0.9577	0.6423	0.5190	0.7115	0.6711	0.5175	0.6286	0.5797
0.1161	9.0	1188	0.5192	0.4859	0.7020	0.5743	0.9518	0.5578	0.5195	0.5992	0.6571	0.4744	0.5532	0.5611
0.1161	10.0	1320	0.5301	0.5478	0.7020	0.6154	0.9563	0.5732	0.5782	0.7619	0.6462	0.4675	0.7253	0.5727
0.1161	11.0	1452	0.4965	0.5139	0.7048	0.5944	0.9531	0.5857	0.5290	0.7189	0.6639	0.4235	0.6476	0.5532
0.049	12.0	1584	0.6207	0.5713	0.6925	0.6261	0.9582	0.64	0.5377	0.7594	0.7207	0.5070	0.6136	0.5530
0.049	13.0	1716	0.6056	0.5360	0.7088	0.6104	0.9570	0.5921	0.5035	0.7000	0.7115	0.4648	0.6939	0.5854
0.049	14.0	1848	0.6540	0.5804	0.6925	0.6315	0.9599	0.6466	0.5344	0.7324	0.6874	0.5401	0.7083	0.5980
0.049	15.0	1980	0.5911	0.5068	0.7048	0.5896	0.9528	0.5399	0.5176	0.7150	0.6397	0.4625	0.6800	0.5865
0.0225	16.0	2112	0.5788	0.5186	0.7007	0.5961	0.9531	0.5874	0.5011	0.7177	0.6796	0.4810	0.6744	0.5517
0.0225	17.0	2244	0.6097	0.5399	0.6912	0.6062	0.9547	0.5811	0.5744	0.6900	0.6439	0.5033	0.7253	0.5470
0.0225	18.0	2376	0.7006	0.5714	0.6748	0.6188	0.9590	0.6471	0.5645	0.6465	0.6710	0.5426	0.6809	0.5755
0.0149	19.0	2508	0.6051	0.5400	0.7252	0.6190	0.9554	0.6443	0.5514	0.6547	0.6777	0.5132	0.6947	0.6
0.0149	20.0	2640	0.7220	0.5995	0.6884	0.6409	0.9605	0.6429	0.5570	0.6806	0.7339	0.5865	0.7416	0.5540
0.0149	21.0	2772	0.6912	0.5977	0.7034	0.6462	0.9599	0.6377	0.5387	0.7343	0.7281	0.5846	0.7273	0.5899
0.0149	22.0	2904	0.6952	0.5802	0.6939	0.6320	0.9574	0.5867	0.5445	0.7358	0.6951	0.5736	0.7473	0.5830
0.0097	23.0	3036	0.7600	0.6241	0.6912	0.6559	0.9618	0.6119	0.5895	0.7629	0.7356	0.5512	0.6897	0.5837
0.0097	24.0	3168	0.7184	0.5924	0.6980	0.6408	0.9598	0.6486	0.5640	0.7179	0.7146	0.5630	0.7174	0.5714
0.0097	25.0	3300	0.7120	0.5485	0.7007	0.6153	0.9566	0.6579	0.5441	0.6667	0.6993	0.4774	0.6522	0.5766
0.0097	26.0	3432	0.7914	0.6009	0.7088	0.6504	0.9583	0.6443	0.6070	0.7293	0.7082	0.5645	0.6737	0.5872
0.0065	27.0	3564	0.7986	0.5800	0.6952	0.6324	0.9589	0.6309	0.5521	0.7150	0.7281	0.4844	0.7097	0.5714
0.0065	28.0	3696	0.7767	0.6087	0.7007	0.6515	0.9599	0.6364	0.5824	0.7526	0.7169	0.5238	0.7097	0.6038
0.0065	29.0	3828	0.7435	0.6077	0.6912	0.6467	0.9612	0.6479	0.5674	0.7396	0.7088	0.5255	0.7333	0.6066
0.0065	30.0	3960	0.8305	0.6230	0.6857	0.6528	0.9613	0.6483	0.5650	0.7817	0.7341	0.4715	0.7174	0.5962
0.0051	31.0	4092	0.7180	0.5776	0.7088	0.6365	0.9583	0.6194	0.5825	0.7393	0.6874	0.4923	0.7021	0.5962
0.0051	32.0	4224	0.7526	0.5708	0.6857	0.6230	0.9585	0.64	0.5276	0.7246	0.7083	0.4627	0.6813	0.5922
0.0051	33.0	4356	0.7548	0.5582	0.7048	0.6230	0.9578	0.6225	0.5707	0.6796	0.6812	0.5039	0.7097	0.5776

Framework versions

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