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2020-Q2-75p-filtered

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metadata
base_model: DouglasPontes/2020-Q1-filtered_tweets
tags:
  - generated_from_trainer
model-index:
  - name: 2020-Q2-75p-filtered
    results: []

2020-Q2-75p-filtered

This model is a fine-tuned version of DouglasPontes/2020-Q1-filtered_tweets on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.2312

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 2.4865
2.6592 0.04 16000 2.4598
2.6592 0.07 24000 2.4472
2.6211 0.09 32000 2.4341
2.6211 0.11 40000 2.4223
2.6048 0.13 48000 2.4217
2.6048 0.16 56000 2.4184
2.5861 0.18 64000 2.4062
2.5861 0.2 72000 2.3919
2.5736 0.22 80000 2.3896
2.5736 0.25 88000 2.3951
2.5559 0.27 96000 2.3903
2.5559 0.29 104000 2.3836
2.5551 0.31 112000 2.3749
2.5551 0.34 120000 2.3794
2.5371 0.36 128000 2.3733
2.5371 0.38 136000 2.3703
2.5417 0.4 144000 2.3662
2.5417 0.43 152000 2.3728
2.5316 0.45 160000 2.3643
2.5316 0.47 168000 2.3568
2.5296 0.49 176000 2.3555
2.5296 0.52 184000 2.3506
2.5215 0.54 192000 2.3482
2.5215 0.56 200000 2.3514
2.5274 0.58 208000 2.3531
2.5274 0.61 216000 2.3463
2.5215 0.63 224000 2.3470
2.5215 0.65 232000 2.3407
2.5096 0.67 240000 2.3400
2.5096 0.7 248000 2.3402
2.5176 0.72 256000 2.3308
2.5176 0.74 264000 2.3342
2.5048 0.76 272000 2.3333
2.5048 0.79 280000 2.3288
2.4979 0.81 288000 2.3298
2.4979 0.83 296000 2.3237
2.4963 0.85 304000 2.3266
2.4963 0.88 312000 2.3197
2.4972 0.9 320000 2.3271
2.4972 0.92 328000 2.3275
2.4969 0.94 336000 2.3210
2.4969 0.97 344000 2.3222
2.4961 0.99 352000 2.3242
2.4961 1.01 360000 2.3155
2.49 1.03 368000 2.3175
2.49 1.06 376000 2.3076
2.4847 1.08 384000 2.3138
2.4847 1.1 392000 2.3183
2.4767 1.12 400000 2.3118
2.4767 1.15 408000 2.3152
2.4788 1.17 416000 2.3089
2.4788 1.19 424000 2.3051
2.4738 1.21 432000 2.3102
2.4738 1.24 440000 2.3069
2.4635 1.26 448000 2.3004
2.4635 1.28 456000 2.3066
2.4828 1.3 464000 2.3078
2.4828 1.32 472000 2.3072
2.4675 1.35 480000 2.3073
2.4675 1.37 488000 2.3014
2.4676 1.39 496000 2.2987
2.4676 1.41 504000 2.2988
2.4678 1.44 512000 2.2971
2.4678 1.46 520000 2.2969
2.4634 1.48 528000 2.2990
2.4634 1.5 536000 2.2869
2.4657 1.53 544000 2.2936
2.4657 1.55 552000 2.2915
2.4607 1.57 560000 2.2903
2.4607 1.59 568000 2.2934
2.4558 1.62 576000 2.2845
2.4558 1.64 584000 2.2897
2.4662 1.66 592000 2.2928
2.4662 1.68 600000 2.2861
2.4658 1.71 608000 2.2883
2.4658 1.73 616000 2.2878
2.4533 1.75 624000 2.2892
2.4533 1.77 632000 2.2886
2.4575 1.8 640000 2.2894
2.4575 1.82 648000 2.2871
2.4565 1.84 656000 2.2798
2.4565 1.86 664000 2.2877
2.4548 1.89 672000 2.2859
2.4548 1.91 680000 2.2787
2.4507 1.93 688000 2.2780
2.4507 1.95 696000 2.2826
2.4455 1.98 704000 2.2838
2.4455 2.0 712000 2.2764
2.4516 2.02 720000 2.2814
2.4516 2.04 728000 2.2807
2.445 2.07 736000 2.2740
2.445 2.09 744000 2.2780
2.4466 2.11 752000 2.2775
2.4466 2.13 760000 2.2783
2.4476 2.16 768000 2.2763
2.4476 2.18 776000 2.2737
2.4449 2.2 784000 2.2753
2.4449 2.22 792000 2.2762
2.4424 2.25 800000 2.2767
2.4424 2.27 808000 2.2702
2.4528 2.29 816000 2.2655
2.4528 2.31 824000 2.2727
2.4523 2.34 832000 2.2733
2.4523 2.36 840000 2.2654
2.4395 2.38 848000 2.2674
2.4395 2.4 856000 2.2754
2.434 2.43 864000 2.2722
2.434 2.45 872000 2.2666
2.4407 2.47 880000 2.2656
2.4407 2.49 888000 2.2654
2.4352 2.52 896000 2.2630
2.4352 2.54 904000 2.2662
2.4393 2.56 912000 2.2692
2.4393 2.58 920000 2.2558
2.4378 2.61 928000 2.2619
2.4378 2.63 936000 2.2614
2.4392 2.65 944000 2.2578
2.4392 2.67 952000 2.2672
2.437 2.69 960000 2.2598
2.437 2.72 968000 2.2633
2.4388 2.74 976000 2.2566
2.4388 2.76 984000 2.2551
2.4386 2.78 992000 2.2606
2.4386 2.81 1000000 2.2634
2.4402 2.83 1008000 2.2641
2.4402 2.85 1016000 2.2619
2.4442 2.87 1024000 2.2584
2.4442 2.9 1032000 2.2579
2.4327 2.92 1040000 2.2523
2.4327 2.94 1048000 2.2562
2.4289 2.96 1056000 2.2593
2.4289 2.99 1064000 2.2562
2.4319 3.01 1072000 2.2536
2.4319 3.03 1080000 2.2603
2.4174 3.05 1088000 2.2549
2.4174 3.08 1096000 2.2595
2.4155 3.1 1104000 2.2555
2.4155 3.12 1112000 2.2501
2.427 3.14 1120000 2.2528
2.427 3.17 1128000 2.2529
2.4222 3.19 1136000 2.2536
2.4222 3.21 1144000 2.2582
2.4232 3.23 1152000 2.2522
2.4232 3.26 1160000 2.2525
2.4252 3.28 1168000 2.2538
2.4252 3.3 1176000 2.2512
2.4209 3.32 1184000 2.2557
2.4209 3.35 1192000 2.2445
2.4243 3.37 1200000 2.2570
2.4243 3.39 1208000 2.2539
2.4278 3.41 1216000 2.2514
2.4278 3.44 1224000 2.2454
2.4286 3.46 1232000 2.2463
2.4286 3.48 1240000 2.2506
2.4274 3.5 1248000 2.2427
2.4274 3.53 1256000 2.2535
2.4201 3.55 1264000 2.2517
2.4201 3.57 1272000 2.2436
2.4233 3.59 1280000 2.2430
2.4233 3.62 1288000 2.2470
2.4183 3.64 1296000 2.2446
2.4183 3.66 1304000 2.2539
2.428 3.68 1312000 2.2492
2.428 3.71 1320000 2.2544
2.4206 3.73 1328000 2.2478
2.4206 3.75 1336000 2.2420
2.4287 3.77 1344000 2.2442
2.4287 3.8 1352000 2.2426
2.4297 3.82 1360000 2.2426
2.4297 3.84 1368000 2.2481
2.4185 3.86 1376000 2.2449
2.4185 3.89 1384000 2.2468
2.4217 3.91 1392000 2.2467
2.4217 3.93 1400000 2.2463
2.4144 3.95 1408000 2.2482
2.4144 3.97 1416000 2.2424
2.4175 4.0 1424000 2.2415
2.4175 4.02 1432000 2.2451
2.4169 4.04 1440000 2.2443
2.4169 4.06 1448000 2.2389
2.4142 4.09 1456000 2.2377
2.4142 4.11 1464000 2.2399
2.4122 4.13 1472000 2.2447
2.4122 4.15 1480000 2.2456
2.4166 4.18 1488000 2.2451
2.4166 4.2 1496000 2.2369
2.4165 4.22 1504000 2.2426
2.4165 4.24 1512000 2.2384
2.4204 4.27 1520000 2.2454
2.4204 4.29 1528000 2.2422
2.4192 4.31 1536000 2.2423
2.4192 4.33 1544000 2.2435
2.4167 4.36 1552000 2.2451
2.4167 4.38 1560000 2.2443
2.4124 4.4 1568000 2.2430
2.4124 4.42 1576000 2.2422
2.406 4.45 1584000 2.2357
2.406 4.47 1592000 2.2395
2.4166 4.49 1600000 2.2378
2.4166 4.51 1608000 2.2420
2.4144 4.54 1616000 2.2402
2.4144 4.56 1624000 2.2384
2.4219 4.58 1632000 2.2438
2.4219 4.6 1640000 2.2455
2.4061 4.63 1648000 2.2397
2.4061 4.65 1656000 2.2354
2.411 4.67 1664000 2.2393
2.411 4.69 1672000 2.2388
2.4125 4.72 1680000 2.2406
2.4125 4.74 1688000 2.2330
2.4092 4.76 1696000 2.2336
2.4092 4.78 1704000 2.2398
2.4078 4.81 1712000 2.2368
2.4078 4.83 1720000 2.2361
2.4185 4.85 1728000 2.2378
2.4185 4.87 1736000 2.2339
2.4088 4.9 1744000 2.2366
2.4088 4.92 1752000 2.2385
2.4095 4.94 1760000 2.2337
2.4095 4.96 1768000 2.2413
2.4078 4.99 1776000 2.2377
2.4078 5.01 1784000 2.2302
2.4073 5.03 1792000 2.2357
2.4073 5.05 1800000 2.2384
2.4073 5.08 1808000 2.2322
2.4073 5.1 1816000 2.2344
2.4043 5.12 1824000 2.2327
2.4043 5.14 1832000 2.2350
2.4082 5.17 1840000 2.2376
2.4082 5.19 1848000 2.2363
2.4073 5.21 1856000 2.2323
2.4073 5.23 1864000 2.2419
2.4148 5.26 1872000 2.2293
2.4148 5.28 1880000 2.2346
2.4098 5.3 1888000 2.2372
2.4098 5.32 1896000 2.2371
2.407 5.34 1904000 2.2397
2.407 5.37 1912000 2.2300
2.4108 5.39 1920000 2.2317
2.4108 5.41 1928000 2.2350
2.4168 5.43 1936000 2.2343
2.4168 5.46 1944000 2.2327
2.4113 5.48 1952000 2.2363
2.4113 5.5 1960000 2.2314
2.4131 5.52 1968000 2.2303
2.4131 5.55 1976000 2.2353
2.4129 5.57 1984000 2.2353
2.4129 5.59 1992000 2.2296
2.4129 5.61 2000000 2.2314
2.4129 5.64 2008000 2.2288
2.4045 5.66 2016000 2.2347
2.4045 5.68 2024000 2.2349
2.4089 5.7 2032000 2.2310
2.4089 5.73 2040000 2.2342
2.4091 5.75 2048000 2.2320
2.4091 5.77 2056000 2.2311
2.4137 5.79 2064000 2.2278
2.4137 5.82 2072000 2.2344
2.4063 5.84 2080000 2.2339
2.4063 5.86 2088000 2.2271
2.4046 5.88 2096000 2.2263
2.4046 5.91 2104000 2.2369
2.4105 5.93 2112000 2.2330
2.4105 5.95 2120000 2.2361
2.4045 5.97 2128000 2.2320
2.4045 6.0 2136000 2.2283
2.4093 6.02 2144000 2.2262
2.4093 6.04 2152000 2.2294
2.4109 6.06 2160000 2.2334
2.4109 6.09 2168000 2.2363
2.4061 6.11 2176000 2.2309
2.4061 6.13 2184000 2.2269
2.4007 6.15 2192000 2.2369
2.4007 6.18 2200000 2.2297
2.4034 6.2 2208000 2.2267
2.4034 6.22 2216000 2.2310
2.4049 6.24 2224000 2.2362
2.4049 6.27 2232000 2.2319
2.4052 6.29 2240000 2.2308
2.4052 6.31 2248000 2.2225
2.4102 6.33 2256000 2.2366
2.4102 6.36 2264000 2.2327
2.4046 6.38 2272000 2.2305
2.4046 6.4 2280000 2.2309
2.4066 6.42 2288000 2.2291
2.4066 6.45 2296000 2.2301
2.4041 6.47 2304000 2.2378
2.4041 6.49 2312000 2.2317
2.4081 6.51 2320000 2.2326
2.4081 6.54 2328000 2.2412
2.4147 6.56 2336000 2.2349
2.4147 6.58 2344000 2.2296
2.4105 6.6 2352000 2.2313
2.4105 6.62 2360000 2.2297
2.4096 6.65 2368000 2.2241
2.4096 6.67 2376000 2.2322
2.4089 6.69 2384000 2.2344
2.4089 6.71 2392000 2.2291
2.4048 6.74 2400000 2.2274

Framework versions

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