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This model is a fine-tuned version of cointegrated/rubert-tiny2 on the None dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| 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 |
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