2020-Q2-75p-filtered_combined90

DouglasPontes/2020-Q2-75p-filtered_combined90

2020-Q2-75p-filtered_combined90 is a fill-mask model from DouglasPontes. Use it when you need the model to fill a missing word. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Fill-Mask
Library: transformers
Model type: roberta
Access: Public
Created: Jan 19, 2024
Updated: Jan 22, 2024
SHA: 07d6adbd

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Base models

DouglasPontes/2020-Q1-filtered_tweets

Model card

From the Hugging Face model README

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

Task: Fill-Mask
Library: transformers
Type: roberta
Created: Jan 19, 2024
Updated: Jan 22, 2024