rubert-base-srl-seqlabeling

Rexhaif/rubert-base-srl-seqlabeling

rubert-base-srl-seqlabeling is a token classification model from Rexhaif. Use it when you need labels on individual words, such as names. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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Model card

From the Hugging Face model README

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 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	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	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	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	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	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	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	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	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	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	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

Task: Token Classification
Library: transformers
Type: bert
Created: Mar 2, 2022
Updated: Sep 20, 2023