DouglasPontes/2020-Q2-75p-filtered_combined75

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