rubert-base-srl-seqlabeling

This model is a fine-tuned version of ./ruBert-base/ on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1723
• Causator Precision: 0.8539
• Causator Recall: 0.8352
• Causator F1: 0.8444
• Causator Number: 91
• Expiriencer Precision: 0.9259
• Expiriencer Recall: 0.9740
• Expiriencer F1: 0.9494
• Expiriencer Number: 77
• Instrument Precision: 0.375
• Instrument Recall: 1.0
• Instrument F1: 0.5455
• Instrument Number: 3
• Other Precision: 0.0
• Other Recall: 0.0
• Other F1: 0.0
• Other Number: 1
• Predicate Precision: 0.9352
• Predicate Recall: 0.9902
• Predicate F1: 0.9619
• Predicate Number: 102
• Overall Precision: 0.8916
• Overall Recall: 0.9307
• Overall F1: 0.9107
• Overall Accuracy: 0.9667

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: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.06
• num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Causator Precision	Causator Recall	Causator F1	Causator Number	Expiriencer Precision	Expiriencer Recall	Expiriencer F1	Expiriencer Number	Instrument Precision	Instrument Recall	Instrument F1	Instrument Number	Other Precision	Other Recall	Other F1	Other Number	Predicate Precision	Predicate Recall	Predicate F1	Predicate Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.2552	1.0	56	0.3471	0.8841	0.6703	0.7625	91	0.8421	0.8312	0.8366	77	0.0	0.0	0.0	3	0.0	0.0	0.0	1	0.9259	0.9804	0.9524	102	0.8893	0.8212	0.8539	0.9203
0.2385	2.0	112	0.1608	0.9103	0.7802	0.8402	91	0.9375	0.9740	0.9554	77	0.2857	0.6667	0.4	3	0.0	0.0	0.0	1	0.9519	0.9706	0.9612	102	0.9182	0.9015	0.9098	0.9554
0.0367	3.0	168	0.1311	0.8902	0.8022	0.8439	91	0.9375	0.9740	0.9554	77	0.4286	1.0	0.6	3	0.0	0.0	0.0	1	0.9709	0.9804	0.9756	102	0.9228	0.9161	0.9194	0.9673
0.0494	4.0	224	0.1507	0.7812	0.8242	0.8021	91	0.9241	0.9481	0.9359	77	0.4286	1.0	0.6	3	0.0	0.0	0.0	1	0.9524	0.9804	0.9662	102	0.8746	0.9161	0.8948	0.9637
0.0699	5.0	280	0.1830	0.8276	0.7912	0.8090	91	0.8941	0.9870	0.9383	77	0.375	1.0	0.5455	3	0.0	0.0	0.0	1	0.9352	0.9902	0.9619	102	0.875	0.9197	0.8968	0.9560
0.0352	6.0	336	0.1994	0.7857	0.8462	0.8148	91	0.9048	0.9870	0.9441	77	0.375	1.0	0.5455	3	0.0	0.0	0.0	1	0.9266	0.9902	0.9573	102	0.8595	0.9380	0.8970	0.9572
0.0186	7.0	392	0.1657	0.8652	0.8462	0.8556	91	0.9146	0.9740	0.9434	77	0.375	1.0	0.5455	3	0.0	0.0	0.0	1	0.9352	0.9902	0.9619	102	0.8920	0.9343	0.9127	0.9673
0.0052	8.0	448	0.1716	0.8556	0.8462	0.8508	91	0.9259	0.9740	0.9494	77	0.375	1.0	0.5455	3	0.0	0.0	0.0	1	0.9352	0.9902	0.9619	102	0.8920	0.9343	0.9127	0.9673
0.0094	9.0	504	0.1715	0.8444	0.8352	0.8398	91	0.9259	0.9740	0.9494	77	0.4286	1.0	0.6	3	0.0	0.0	0.0	1	0.9352	0.9902	0.9619	102	0.8916	0.9307	0.9107	0.9667
0.0078	10.0	560	0.1723	0.8539	0.8352	0.8444	91	0.9259	0.9740	0.9494	77	0.375	1.0	0.5455	3	0.0	0.0	0.0	1	0.9352	0.9902	0.9619	102	0.8916	0.9307	0.9107	0.9667

Framework versions

• Transformers 4.13.0.dev0
• Pytorch 1.10.0+cu102
• Datasets 1.15.1
• Tokenizers 0.10.3