viorel123/dt-classifier
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1870
- Validation Loss: 0.2287
- Epoch: 98
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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 6500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
Train Loss | Validation Loss | Epoch |
---|---|---|
0.5182 | 0.3906 | 0 |
0.3398 | 0.3523 | 1 |
0.3172 | 0.3509 | 2 |
0.3120 | 0.3493 | 3 |
0.3101 | 0.3482 | 4 |
0.3079 | 0.3424 | 5 |
0.3061 | 0.3411 | 6 |
0.3024 | 0.3370 | 7 |
0.3005 | 0.3336 | 8 |
0.2958 | 0.3291 | 9 |
0.2952 | 0.3262 | 10 |
0.2916 | 0.3229 | 11 |
0.2873 | 0.3200 | 12 |
0.2867 | 0.3162 | 13 |
0.2816 | 0.3124 | 14 |
0.2783 | 0.3086 | 15 |
0.2743 | 0.3068 | 16 |
0.2730 | 0.3032 | 17 |
0.2686 | 0.2983 | 18 |
0.2666 | 0.2942 | 19 |
0.2648 | 0.2922 | 20 |
0.2628 | 0.2914 | 21 |
0.2553 | 0.2865 | 22 |
0.2541 | 0.2833 | 23 |
0.2525 | 0.2826 | 24 |
0.2489 | 0.2786 | 25 |
0.2476 | 0.2765 | 26 |
0.2436 | 0.2748 | 27 |
0.2428 | 0.2743 | 28 |
0.2387 | 0.2698 | 29 |
0.2404 | 0.2685 | 30 |
0.2318 | 0.2649 | 31 |
0.2329 | 0.2636 | 32 |
0.2282 | 0.2625 | 33 |
0.2290 | 0.2597 | 34 |
0.2299 | 0.2579 | 35 |
0.2271 | 0.2546 | 36 |
0.2237 | 0.2530 | 37 |
0.2228 | 0.2548 | 38 |
0.2170 | 0.2518 | 39 |
0.2180 | 0.2526 | 40 |
0.2172 | 0.2504 | 41 |
0.2121 | 0.2492 | 42 |
0.2142 | 0.2464 | 43 |
0.2143 | 0.2468 | 44 |
0.2099 | 0.2460 | 45 |
0.2065 | 0.2441 | 46 |
0.2064 | 0.2424 | 47 |
0.2085 | 0.2453 | 48 |
0.2082 | 0.2428 | 49 |
0.2024 | 0.2407 | 50 |
0.2045 | 0.2413 | 51 |
0.2040 | 0.2411 | 52 |
0.2009 | 0.2401 | 53 |
0.2011 | 0.2376 | 54 |
0.2001 | 0.2374 | 55 |
0.1986 | 0.2367 | 56 |
0.2030 | 0.2365 | 57 |
0.1971 | 0.2374 | 58 |
0.1993 | 0.2351 | 59 |
0.2003 | 0.2348 | 60 |
0.1989 | 0.2345 | 61 |
0.2010 | 0.2347 | 62 |
0.1977 | 0.2336 | 63 |
0.1933 | 0.2343 | 64 |
0.1932 | 0.2341 | 65 |
0.1934 | 0.2338 | 66 |
0.1894 | 0.2331 | 67 |
0.1937 | 0.2330 | 68 |
0.1928 | 0.2324 | 69 |
0.1901 | 0.2314 | 70 |
0.1914 | 0.2327 | 71 |
0.1926 | 0.2317 | 72 |
0.1911 | 0.2316 | 73 |
0.1885 | 0.2312 | 74 |
0.1910 | 0.2317 | 75 |
0.1845 | 0.2311 | 76 |
0.1896 | 0.2311 | 77 |
0.1864 | 0.2299 | 78 |
0.1922 | 0.2299 | 79 |
0.1880 | 0.2302 | 80 |
0.1878 | 0.2300 | 81 |
0.1847 | 0.2302 | 82 |
0.1880 | 0.2300 | 83 |
0.1822 | 0.2298 | 84 |
0.1795 | 0.2297 | 85 |
0.1869 | 0.2297 | 86 |
0.1820 | 0.2295 | 87 |
0.1826 | 0.2292 | 88 |
0.1864 | 0.2292 | 89 |
0.1876 | 0.2288 | 90 |
0.1857 | 0.2289 | 91 |
0.1855 | 0.2286 | 92 |
0.1858 | 0.2288 | 93 |
0.1862 | 0.2286 | 94 |
0.1858 | 0.2287 | 95 |
0.1811 | 0.2289 | 96 |
0.1836 | 0.2287 | 97 |
0.1870 | 0.2287 | 98 |
Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
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