metadata

license: mit
base_model: cointegrated/rubert-tiny2
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: rubert-tiny2-odonata-extended-305-1-ner
    results: []

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: 0.0181
Precision: 0.7075
Recall: 0.2799
F1: 0.4011
Accuracy: 0.9963

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	213	0.0343	0.0	0.0	0.0	0.9952
No log	2.0	426	0.0219	0.0	0.0	0.0	0.9952
0.045	3.0	639	0.0151	0.0	0.0	0.0	0.9952
0.045	4.0	852	0.0121	0.7727	0.1269	0.2179	0.9957
0.0065	5.0	1065	0.0127	0.6296	0.1269	0.2112	0.9957
0.0065	6.0	1278	0.0116	0.6667	0.2463	0.3597	0.9962
0.0065	7.0	1491	0.0107	0.6696	0.2873	0.4021	0.9964
0.0047	8.0	1704	0.0115	0.7158	0.2537	0.3747	0.9963
0.0047	9.0	1917	0.0117	0.7327	0.2761	0.4011	0.9963
0.0037	10.0	2130	0.0115	0.675	0.3022	0.4175	0.9964
0.0037	11.0	2343	0.0128	0.6990	0.2687	0.3881	0.9963
0.0032	12.0	2556	0.0136	0.6931	0.2612	0.3794	0.9963
0.0032	13.0	2769	0.0136	0.7	0.2873	0.4074	0.9963
0.0032	14.0	2982	0.0132	0.6774	0.3134	0.4286	0.9964
0.0026	15.0	3195	0.0137	0.6942	0.3134	0.4319	0.9963
0.0026	16.0	3408	0.0140	0.7193	0.3060	0.4293	0.9964
0.0022	17.0	3621	0.0144	0.6991	0.2948	0.4147	0.9964
0.0022	18.0	3834	0.0157	0.7156	0.2910	0.4138	0.9964
0.0019	19.0	4047	0.0166	0.6923	0.2351	0.3510	0.9962
0.0019	20.0	4260	0.0163	0.72	0.2687	0.3913	0.9963
0.0019	21.0	4473	0.0159	0.6957	0.2985	0.4178	0.9963
0.0017	22.0	4686	0.0165	0.6696	0.2873	0.4021	0.9962
0.0017	23.0	4899	0.0174	0.6952	0.2724	0.3914	0.9963
0.0015	24.0	5112	0.0180	0.6882	0.2388	0.3546	0.9961
0.0015	25.0	5325	0.0184	0.6915	0.2425	0.3591	0.9962
0.0014	26.0	5538	0.0183	0.7041	0.2575	0.3770	0.9962
0.0014	27.0	5751	0.0177	0.7009	0.2799	0.4000	0.9963
0.0014	28.0	5964	0.0180	0.7075	0.2799	0.4011	0.9963
0.0013	29.0	6177	0.0178	0.6991	0.2948	0.4147	0.9963
0.0013	30.0	6390	0.0181	0.7075	0.2799	0.4011	0.9963

Framework versions

Transformers 4.41.2
Pytorch 2.3.1+cpu
Datasets 2.19.2
Tokenizers 0.19.1