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2020-Q1-25p-filtered

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2019-90m on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.2233

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: 4.1e-07
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 2400000

Training results

Training Loss Epoch Step Validation Loss
No log 0.02 8000 2.5883
2.7561 0.04 16000 2.4958
2.7561 0.07 24000 2.4376
2.531 0.09 32000 2.4090
2.531 0.11 40000 2.3791
2.4627 0.13 48000 2.3666
2.4627 0.15 56000 2.3457
2.4252 0.17 64000 2.3380
2.4252 0.2 72000 2.3298
2.4061 0.22 80000 2.3253
2.4061 0.24 88000 2.3177
2.395 0.26 96000 2.3131
2.395 0.28 104000 2.3058
2.3843 0.31 112000 2.3010
2.3843 0.33 120000 2.2925
2.3738 0.35 128000 2.2916
2.3738 0.37 136000 2.2947
2.3686 0.39 144000 2.2835
2.3686 0.42 152000 2.2864
2.3615 0.44 160000 2.2834
2.3615 0.46 168000 2.2768
2.3515 0.48 176000 2.2803
2.3515 0.5 184000 2.2804
2.3508 0.52 192000 2.2754
2.3508 0.55 200000 2.2767
2.35 0.57 208000 2.2742
2.35 0.59 216000 2.2722
2.3385 0.61 224000 2.2661
2.3385 0.63 232000 2.2706
2.3393 0.66 240000 2.2633
2.3393 0.68 248000 2.2648
2.3392 0.7 256000 2.2656
2.3392 0.72 264000 2.2660
2.3336 0.74 272000 2.2657
2.3336 0.76 280000 2.2605
2.3324 0.79 288000 2.2615
2.3324 0.81 296000 2.2551
2.3312 0.83 304000 2.2581
2.3312 0.85 312000 2.2626
2.3352 0.87 320000 2.2576
2.3352 0.9 328000 2.2553
2.3287 0.92 336000 2.2591
2.3287 0.94 344000 2.2558
2.321 0.96 352000 2.2603
2.321 0.98 360000 2.2569
2.3278 1.01 368000 2.2544
2.3278 1.03 376000 2.2604
2.319 1.05 384000 2.2535
2.319 1.07 392000 2.2420
2.3151 1.09 400000 2.2583
2.3151 1.11 408000 2.2535
2.3144 1.14 416000 2.2582
2.3144 1.16 424000 2.2496
2.3191 1.18 432000 2.2532
2.3191 1.2 440000 2.2515
2.3168 1.22 448000 2.2501
2.3168 1.25 456000 2.2453
2.3156 1.27 464000 2.2445
2.3156 1.29 472000 2.2485
2.3178 1.31 480000 2.2493
2.3178 1.33 488000 2.2443
2.3113 1.35 496000 2.2493
2.3113 1.38 504000 2.2493
2.3116 1.4 512000 2.2483
2.3116 1.42 520000 2.2459
2.3166 1.44 528000 2.2481
2.3166 1.46 536000 2.2542
2.3158 1.49 544000 2.2443
2.3158 1.51 552000 2.2402
2.3148 1.53 560000 2.2449
2.3148 1.55 568000 2.2415
2.3145 1.57 576000 2.2471
2.3145 1.6 584000 2.2469
2.3119 1.62 592000 2.2445
2.3119 1.64 600000 2.2487
2.3045 1.66 608000 2.2456
2.3045 1.68 616000 2.2466
2.3046 1.7 624000 2.2357
2.3046 1.73 632000 2.2448
2.3083 1.75 640000 2.2381
2.3083 1.77 648000 2.2439
2.3065 1.79 656000 2.2402
2.3065 1.81 664000 2.2439
2.307 1.84 672000 2.2409
2.307 1.86 680000 2.2426
2.3026 1.88 688000 2.2387
2.3026 1.9 696000 2.2357
2.2949 1.92 704000 2.2379
2.2949 1.95 712000 2.2408
2.2951 1.97 720000 2.2432
2.2951 1.99 728000 2.2444
2.3011 2.01 736000 2.2382
2.3011 2.03 744000 2.2391
2.3017 2.05 752000 2.2363
2.3017 2.08 760000 2.2444
2.2978 2.1 768000 2.2370
2.2978 2.12 776000 2.2350
2.2961 2.14 784000 2.2347
2.2961 2.16 792000 2.2386
2.2968 2.19 800000 2.2322
2.2968 2.21 808000 2.2403
2.2962 2.23 816000 2.2347
2.2962 2.25 824000 2.2398
2.2984 2.27 832000 2.2358
2.2984 2.29 840000 2.2412
2.3029 2.32 848000 2.2386
2.3029 2.34 856000 2.2346
2.2985 2.36 864000 2.2323
2.2985 2.38 872000 2.2387
2.2922 2.4 880000 2.2303
2.2922 2.43 888000 2.2326
2.2967 2.45 896000 2.2422
2.2967 2.47 904000 2.2350
2.2917 2.49 912000 2.2299
2.2917 2.51 920000 2.2308
2.2912 2.54 928000 2.2345
2.2912 2.56 936000 2.2264
2.2887 2.58 944000 2.2361
2.2887 2.6 952000 2.2319
2.2956 2.62 960000 2.2340
2.2956 2.64 968000 2.2356
2.2927 2.67 976000 2.2366
2.2927 2.69 984000 2.2335
2.2872 2.71 992000 2.2330
2.2872 2.73 1000000 2.2251
2.2936 2.75 1008000 2.2327
2.2936 2.78 1016000 2.2326
2.2899 2.8 1024000 2.2307
2.2899 2.82 1032000 2.2291
2.2931 2.84 1040000 2.2285
2.2931 2.86 1048000 2.2327
2.3042 2.88 1056000 2.2367
2.3042 2.91 1064000 2.2345
2.2864 2.93 1072000 2.2267
2.2864 2.95 1080000 2.2343
2.2933 2.97 1088000 2.2354
2.2933 2.99 1096000 2.2260
2.2909 3.02 1104000 2.2341
2.2909 3.04 1112000 2.2266
2.2889 3.06 1120000 2.2253
2.2889 3.08 1128000 2.2255
2.292 3.1 1136000 2.2194
2.292 3.13 1144000 2.2319
2.282 3.15 1152000 2.2221
2.282 3.17 1160000 2.2273
2.2827 3.19 1168000 2.2296
2.2827 3.21 1176000 2.2332
2.2937 3.23 1184000 2.2302
2.2937 3.26 1192000 2.2262
2.2845 3.28 1200000 2.2318
2.2845 3.3 1208000 2.2291
2.284 3.32 1216000 2.2327
2.284 3.34 1224000 2.2308
2.2923 3.37 1232000 2.2264
2.2923 3.39 1240000 2.2390
2.2859 3.41 1248000 2.2310
2.2859 3.43 1256000 2.2287
2.2879 3.45 1264000 2.2284
2.2879 3.47 1272000 2.2228
2.292 3.5 1280000 2.2296
2.292 3.52 1288000 2.2329
2.2827 3.54 1296000 2.2263
2.2827 3.56 1304000 2.2324
2.2829 3.58 1312000 2.2232
2.2829 3.61 1320000 2.2273
2.2863 3.63 1328000 2.2296
2.2863 3.65 1336000 2.2294
2.2796 3.67 1344000 2.2283
2.2796 3.69 1352000 2.2280
2.2835 3.72 1360000 2.2264
2.2835 3.74 1368000 2.2224
2.2875 3.76 1376000 2.2219
2.2875 3.78 1384000 2.2243
2.2792 3.8 1392000 2.2320
2.2792 3.82 1400000 2.2273
2.2932 3.85 1408000 2.2257
2.2932 3.87 1416000 2.2360
2.2899 3.89 1424000 2.2277
2.2899 3.91 1432000 2.2275
2.2859 3.93 1440000 2.2287
2.2859 3.96 1448000 2.2211
2.2876 3.98 1456000 2.2236
2.2876 4.0 1464000 2.2288
2.2879 4.02 1472000 2.2226
2.2879 4.04 1480000 2.2242
2.282 4.06 1488000 2.2286
2.282 4.09 1496000 2.2210
2.2828 4.11 1504000 2.2304
2.2828 4.13 1512000 2.2310
2.2765 4.15 1520000 2.2295
2.2765 4.17 1528000 2.2276
2.2839 4.2 1536000 2.2260
2.2839 4.22 1544000 2.2255
2.2845 4.24 1552000 2.2200
2.2845 4.26 1560000 2.2228
2.2816 4.28 1568000 2.2322
2.2816 4.31 1576000 2.2250
2.2965 4.33 1584000 2.2242
2.2965 4.35 1592000 2.2295
2.2806 4.37 1600000 2.2198
2.2806 4.39 1608000 2.2301
2.2868 4.41 1616000 2.2309
2.2868 4.44 1624000 2.2270
2.2907 4.46 1632000 2.2291
2.2907 4.48 1640000 2.2269
2.2809 4.5 1648000 2.2261
2.2809 4.52 1656000 2.2318
2.2876 4.55 1664000 2.2252
2.2876 4.57 1672000 2.2248
2.2844 4.59 1680000 2.2223
2.2844 4.61 1688000 2.2250
2.2841 4.63 1696000 2.2278
2.2841 4.65 1704000 2.2226
2.2851 4.68 1712000 2.2274
2.2851 4.7 1720000 2.2247
2.2863 4.72 1728000 2.2239
2.2863 4.74 1736000 2.2227
2.2788 4.76 1744000 2.2234
2.2788 4.79 1752000 2.2293
2.2849 4.81 1760000 2.2199
2.2849 4.83 1768000 2.2309
2.2826 4.85 1776000 2.2235
2.2826 4.87 1784000 2.2292
2.2809 4.9 1792000 2.2248
2.2809 4.92 1800000 2.2187
2.2865 4.94 1808000 2.2331
2.2865 4.96 1816000 2.2244
2.2773 4.98 1824000 2.2246
2.2773 5.0 1832000 2.2315
2.2738 5.03 1840000 2.2319
2.2738 5.05 1848000 2.2258
2.2806 5.07 1856000 2.2241
2.2806 5.09 1864000 2.2228
2.2822 5.11 1872000 2.2218
2.2822 5.14 1880000 2.2276
2.2866 5.16 1888000 2.2233
2.2866 5.18 1896000 2.2266
2.2831 5.2 1904000 2.2231
2.2831 5.22 1912000 2.2241
2.2875 5.24 1920000 2.2263
2.2875 5.27 1928000 2.2234
2.2802 5.29 1936000 2.2269
2.2802 5.31 1944000 2.2253
2.2905 5.33 1952000 2.2191
2.2905 5.35 1960000 2.2217
2.282 5.38 1968000 2.2212
2.282 5.4 1976000 2.2213
2.2798 5.42 1984000 2.2218
2.2798 5.44 1992000 2.2222
2.2864 5.46 2000000 2.2212
2.2864 5.49 2008000 2.2282
2.2867 5.51 2016000 2.2304
2.2867 5.53 2024000 2.2222
2.2834 5.55 2032000 2.2285
2.2834 5.57 2040000 2.2230
2.2851 5.59 2048000 2.2237
2.2851 5.62 2056000 2.2283
2.2774 5.64 2064000 2.2232
2.2774 5.66 2072000 2.2282
2.277 5.68 2080000 2.2271
2.277 5.7 2088000 2.2256
2.2868 5.73 2096000 2.2252
2.2868 5.75 2104000 2.2285
2.2727 5.77 2112000 2.2251
2.2727 5.79 2120000 2.2239
2.2803 5.81 2128000 2.2287
2.2803 5.84 2136000 2.2274
2.2785 5.86 2144000 2.2227
2.2785 5.88 2152000 2.2267
2.2829 5.9 2160000 2.2251
2.2829 5.92 2168000 2.2228
2.2816 5.94 2176000 2.2235
2.2816 5.97 2184000 2.2289
2.283 5.99 2192000 2.2238
2.283 6.01 2200000 2.2245
2.2761 6.03 2208000 2.2297
2.2761 6.05 2216000 2.2300
2.2823 6.08 2224000 2.2268
2.2823 6.1 2232000 2.2252
2.2715 6.12 2240000 2.2240
2.2715 6.14 2248000 2.2233
2.2809 6.16 2256000 2.2238
2.2809 6.18 2264000 2.2204
2.2823 6.21 2272000 2.2218
2.2823 6.23 2280000 2.2295
2.2848 6.25 2288000 2.2298
2.2848 6.27 2296000 2.2299
2.2847 6.29 2304000 2.2246
2.2847 6.32 2312000 2.2230
2.2783 6.34 2320000 2.2260
2.2783 6.36 2328000 2.2176
2.2791 6.38 2336000 2.2211
2.2791 6.4 2344000 2.2262
2.2797 6.43 2352000 2.2293
2.2797 6.45 2360000 2.2219
2.2784 6.47 2368000 2.2249
2.2784 6.49 2376000 2.2216
2.271 6.51 2384000 2.2256
2.271 6.53 2392000 2.2296
2.2787 6.56 2400000 2.2275

Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.5
  • Tokenizers 0.14.0
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