rubert-tiny2-odonata-extended-305-1-ner

This model is a fine-tuned version of cointegrated/rubert-tiny2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0171
• Precision: 0.5782
• Recall: 0.6289
• F1: 0.6025
• Accuracy: 0.9940

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	25	0.2514	0.0	0.0	0.0	0.9913
No log	2.0	50	0.0660	0.0	0.0	0.0	0.9913
No log	3.0	75	0.0579	0.0	0.0	0.0	0.9913
No log	4.0	100	0.0557	0.0	0.0	0.0	0.9913
No log	5.0	125	0.0528	0.0	0.0	0.0	0.9913
No log	6.0	150	0.0465	0.0	0.0	0.0	0.9913
No log	7.0	175	0.0359	1.0	0.0052	0.0103	0.9914
No log	8.0	200	0.0278	0.5802	0.3918	0.4677	0.9921
No log	9.0	225	0.0241	0.5940	0.4072	0.4832	0.9922
No log	10.0	250	0.0223	0.6	0.4175	0.4924	0.9925
No log	11.0	275	0.0212	0.5417	0.4691	0.5028	0.9930
No log	12.0	300	0.0204	0.52	0.4691	0.4932	0.9931
No log	13.0	325	0.0199	0.5579	0.5464	0.5521	0.9936
No log	14.0	350	0.0194	0.5761	0.5464	0.5608	0.9939
No log	15.0	375	0.0190	0.5761	0.5464	0.5608	0.9938
No log	16.0	400	0.0187	0.5670	0.5670	0.5670	0.9939
No log	17.0	425	0.0184	0.5685	0.5773	0.5729	0.9938
No log	18.0	450	0.0182	0.5707	0.6031	0.5865	0.9939
No log	19.0	475	0.0180	0.5680	0.6031	0.5850	0.9940
0.0748	20.0	500	0.0177	0.5764	0.6031	0.5894	0.9941
0.0748	21.0	525	0.0176	0.5907	0.5876	0.5891	0.9941
0.0748	22.0	550	0.0176	0.5769	0.6186	0.5970	0.9941
0.0748	23.0	575	0.0174	0.5939	0.6031	0.5985	0.9942
0.0748	24.0	600	0.0173	0.5854	0.6186	0.6015	0.9942
0.0748	25.0	625	0.0172	0.5902	0.6237	0.6065	0.9942
0.0748	26.0	650	0.0172	0.5865	0.6289	0.6070	0.9942
0.0748	27.0	675	0.0171	0.5862	0.6134	0.5995	0.9941
0.0748	28.0	700	0.0171	0.5882	0.6186	0.6030	0.9942
0.0748	29.0	725	0.0171	0.5817	0.6237	0.6020	0.9941
0.0748	30.0	750	0.0171	0.5782	0.6289	0.6025	0.9940

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.1+cpu
• Datasets 2.19.2
• Tokenizers 0.19.1