rubert-tiny2-russe-toxicity

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: 0.2087
Precision Macro: 0.9395
Recall Macro: 0.9394
F1 Macro: 0.9394
F1 Neutral: 0.9398
F1 Toxic: 0.9390

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: 32
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision Macro	Recall Macro	F1 Macro	F1 Neutral	F1 Toxic
0.6127	0.23	100	0.4477	0.8252	0.825	0.8250	0.8274	0.8226
0.3728	0.46	200	0.2852	0.8877	0.8875	0.8875	0.8886	0.8864
0.2888	0.69	300	0.2405	0.9039	0.9038	0.9037	0.9047	0.9028
0.2724	0.92	400	0.2288	0.9110	0.9094	0.9093	0.9122	0.9064
0.2387	1.15	500	0.2161	0.9132	0.9125	0.9125	0.9143	0.9106
0.2189	1.38	600	0.2098	0.9206	0.9206	0.9206	0.9208	0.9205
0.1783	1.61	700	0.2045	0.9233	0.9231	0.9231	0.9238	0.9224
0.1911	1.84	800	0.2098	0.9184	0.9175	0.9175	0.9155	0.9194
0.1749	2.07	900	0.1947	0.9271	0.9269	0.9269	0.9277	0.9260
0.1728	2.3	1000	0.1893	0.9263	0.9263	0.9262	0.9265	0.9260
0.1628	2.53	1100	0.1926	0.9276	0.9275	0.9275	0.9270	0.9280
0.1545	2.76	1200	0.1889	0.9299	0.9294	0.9294	0.9306	0.9281
0.1659	2.99	1300	0.1893	0.9263	0.9263	0.9263	0.9263	0.9263
0.1433	3.22	1400	0.1952	0.9264	0.9263	0.9262	0.9256	0.9269
0.1243	3.45	1500	0.1838	0.9300	0.93	0.9300	0.9303	0.9296
0.1357	3.68	1600	0.1846	0.9370	0.9356	0.9356	0.9374	0.9338
0.1419	3.91	1700	0.1793	0.9339	0.9331	0.9331	0.9345	0.9317
0.1205	4.14	1800	0.1896	0.9306	0.9306	0.9306	0.9306	0.9307
0.1159	4.37	1900	0.1905	0.9354	0.935	0.9350	0.9360	0.9340
0.1185	4.6	2000	0.1893	0.9400	0.9387	0.9387	0.9403	0.9371
0.102	4.83	2100	0.1944	0.9415	0.9406	0.9406	0.9419	0.9393
0.1222	5.06	2200	0.1880	0.9390	0.9387	0.9387	0.9394	0.9381
0.1006	5.29	2300	0.1964	0.9331	0.9331	0.9331	0.9330	0.9333
0.108	5.52	2400	0.1901	0.9382	0.9381	0.9381	0.9384	0.9379
0.1059	5.75	2500	0.1907	0.9401	0.94	0.9400	0.9404	0.9396
0.1033	5.98	2600	0.1905	0.9409	0.94	0.9400	0.9413	0.9386
0.0969	6.21	2700	0.1969	0.9345	0.9344	0.9344	0.9338	0.9349
0.0826	6.44	2800	0.1952	0.9407	0.9406	0.9406	0.9410	0.9402
0.1055	6.67	2900	0.1967	0.9375	0.9375	0.9375	0.9373	0.9377
0.0982	6.9	3000	0.2063	0.9394	0.9381	0.9381	0.9397	0.9364
0.0862	7.13	3100	0.2056	0.9414	0.9412	0.9412	0.9418	0.9407
0.0811	7.36	3200	0.2054	0.9414	0.9412	0.9412	0.9418	0.9407
0.0908	7.59	3300	0.2078	0.9394	0.9394	0.9394	0.9396	0.9392
0.092	7.82	3400	0.2003	0.9400	0.94	0.9400	0.9401	0.9399
0.0834	8.05	3500	0.2039	0.9394	0.9394	0.9394	0.9392	0.9396
0.0747	8.28	3600	0.2018	0.9413	0.9413	0.9412	0.9415	0.9410
0.0898	8.51	3700	0.2009	0.94	0.94	0.94	0.94	0.94
0.077	8.74	3800	0.2022	0.9410	0.9406	0.9406	0.9415	0.9398
0.087	8.97	3900	0.2055	0.9382	0.9381	0.9381	0.9385	0.9377
0.0647	9.2	4000	0.2062	0.9401	0.94	0.9400	0.9405	0.9395
0.0736	9.43	4100	0.2081	0.9389	0.9387	0.9387	0.9393	0.9382
0.08	9.66	4200	0.2087	0.9395	0.9394	0.9394	0.9399	0.9388
0.083	9.89	4300	0.2087	0.9395	0.9394	0.9394	0.9398	0.9390

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05

train_batch_size: 32

eval_batch_size: 64

seed: 42

optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

lr_scheduler_type: linear

num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision Macro	Recall Macro	F1 Macro	F1 Neutral	F1 Toxic
0.6127	0.23	100	0.4477	0.8252	0.825	0.8250	0.8274	0.8226
0.3728	0.46	200	0.2852	0.8877	0.8875	0.8875	0.8886	0.8864
0.2888	0.69	300	0.2405	0.9039	0.9038	0.9037	0.9047	0.9028
0.2724	0.92	400	0.2288	0.9110	0.9094	0.9093	0.9122	0.9064
0.2387	1.15	500	0.2161	0.9132	0.9125	0.9125	0.9143	0.9106
0.2189	1.38	600	0.2098	0.9206	0.9206	0.9206	0.9208	0.9205
0.1783	1.61	700	0.2045	0.9233	0.9231	0.9231	0.9238	0.9224
0.1911	1.84	800	0.2098	0.9184	0.9175	0.9175	0.9155	0.9194
0.1749	2.07	900	0.1947	0.9271	0.9269	0.9269	0.9277	0.9260
0.1728	2.3	1000	0.1893	0.9263	0.9263	0.9262	0.9265	0.9260
0.1628	2.53	1100	0.1926	0.9276	0.9275	0.9275	0.9270	0.9280
0.1545	2.76	1200	0.1889	0.9299	0.9294	0.9294	0.9306	0.9281
0.1659	2.99	1300	0.1893	0.9263	0.9263	0.9263	0.9263	0.9263
0.1433	3.22	1400	0.1952	0.9264	0.9263	0.9262	0.9256	0.9269
0.1243	3.45	1500	0.1838	0.9300	0.93	0.9300	0.9303	0.9296
0.1357	3.68	1600	0.1846	0.9370	0.9356	0.9356	0.9374	0.9338
0.1419	3.91	1700	0.1793	0.9339	0.9331	0.9331	0.9345	0.9317
0.1205	4.14	1800	0.1896	0.9306	0.9306	0.9306	0.9306	0.9307
0.1159	4.37	1900	0.1905	0.9354	0.935	0.9350	0.9360	0.9340
0.1185	4.6	2000	0.1893	0.9400	0.9387	0.9387	0.9403	0.9371
0.102	4.83	2100	0.1944	0.9415	0.9406	0.9406	0.9419	0.9393
0.1222	5.06	2200	0.1880	0.9390	0.9387	0.9387	0.9394	0.9381
0.1006	5.29	2300	0.1964	0.9331	0.9331	0.9331	0.9330	0.9333
0.108	5.52	2400	0.1901	0.9382	0.9381	0.9381	0.9384	0.9379
0.1059	5.75	2500	0.1907	0.9401	0.94	0.9400	0.9404	0.9396
0.1033	5.98	2600	0.1905	0.9409	0.94	0.9400	0.9413	0.9386
0.0969	6.21	2700	0.1969	0.9345	0.9344	0.9344	0.9338	0.9349
0.0826	6.44	2800	0.1952	0.9407	0.9406	0.9406	0.9410	0.9402
0.1055	6.67	2900	0.1967	0.9375	0.9375	0.9375	0.9373	0.9377
0.0982	6.9	3000	0.2063	0.9394	0.9381	0.9381	0.9397	0.9364
0.0862	7.13	3100	0.2056	0.9414	0.9412	0.9412	0.9418	0.9407
0.0811	7.36	3200	0.2054	0.9414	0.9412	0.9412	0.9418	0.9407
0.0908	7.59	3300	0.2078	0.9394	0.9394	0.9394	0.9396	0.9392
0.092	7.82	3400	0.2003	0.9400	0.94	0.9400	0.9401	0.9399
0.0834	8.05	3500	0.2039	0.9394	0.9394	0.9394	0.9392	0.9396
0.0747	8.28	3600	0.2018	0.9413	0.9413	0.9412	0.9415	0.9410
0.0898	8.51	3700	0.2009	0.94	0.94	0.94	0.94	0.94
0.077	8.74	3800	0.2022	0.9410	0.9406	0.9406	0.9415	0.9398
0.087	8.97	3900	0.2055	0.9382	0.9381	0.9381	0.9385	0.9377
0.0647	9.2	4000	0.2062	0.9401	0.94	0.9400	0.9405	0.9395
0.0736	9.43	4100	0.2081	0.9389	0.9387	0.9387	0.9393	0.9382
0.08	9.66	4200	0.2087	0.9395	0.9394	0.9394	0.9399	0.9388
0.083	9.89	4300	0.2087	0.9395	0.9394	0.9394	0.9398	0.9390

Framework versions

Transformers 4.28.1

Pytorch 2.0.0+cu118

Datasets 2.11.0

Tokenizers 0.13.3

BunnyNoBugs/rubert-tiny2-russe-toxicity

rubert-tiny2-russe-toxicity

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

rubert-tiny2-russe-toxicity

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions