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DouglasPontes/
2020-Q2-75p-filtered_combined90

Name: 2020-Q2-75p-filtered_combined90
Availability: InStock

generated_from_trainer

DouglasPontes/2020-Q1-filtered_tweets

finetune

autotrain_compatible

endpoints_compatible

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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

DouglasPontes/2020-Q2-75p-filtered_combined90

2020-Q2-75p-filtered

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DouglasPontes/
2020-Q2-75p-filtered_combined90