rubert-tiny2-russe-toxicity

BunnyNoBugs/rubert-tiny2-russe-toxicity

rubert-tiny2-russe-toxicity is a text classification model from BunnyNoBugs. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

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

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Apr 24, 2023
Updated: Apr 24, 2023
SHA: b136cda1

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Model card

From the Hugging Face model README

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

Task: Text Classification
Library: transformers
Type: bert
License: mit
Created: Apr 24, 2023
Updated: Apr 24, 2023