Sentiment-google-t5-v1_1-large-inter_model-frequency-human_annots_str

This model is a fine-tuned version of google/t5-v1_1-large on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.2676

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: 0.0001
• train_batch_size: 128
• eval_batch_size: 128
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 200

Training results

Training Loss	Epoch	Step	Validation Loss
20.8675	1.0	44	24.9691
18.2491	2.0	88	16.9536
13.3557	3.0	132	11.4063
11.0809	4.0	176	10.9529
10.1527	5.0	220	10.7258
9.9235	6.0	264	10.5299
9.8499	7.0	308	10.3942
9.5638	8.0	352	10.0279
9.0218	9.0	396	9.4463
8.5704	10.0	440	9.0452
8.3982	11.0	484	8.8619
8.3396	12.0	528	8.7363
7.0951	13.0	572	1.3757
1.1349	14.0	616	1.0516
1.0735	15.0	660	1.0456
1.0787	16.0	704	1.0342
1.0796	17.0	748	1.0342
1.0476	18.0	792	1.0318
1.0566	19.0	836	1.0311
1.0472	20.0	880	1.0247
1.0401	21.0	924	1.0201
1.0725	22.0	968	1.0159
1.0447	23.0	1012	1.0180
1.0477	24.0	1056	1.0121
1.0357	25.0	1100	1.0109
1.0333	26.0	1144	1.0096
1.0282	27.0	1188	1.0078
1.0206	28.0	1232	1.0100
1.0241	29.0	1276	1.0081
1.023	30.0	1320	1.0053
0.9993	31.0	1364	1.0073
1.0104	32.0	1408	1.0079
1.0176	33.0	1452	1.0014
1.0157	34.0	1496	0.9977
1.0204	35.0	1540	0.9960
1.0174	36.0	1584	0.9967
1.0252	37.0	1628	0.9949
1.0076	38.0	1672	0.9913
1.0137	39.0	1716	0.9874
1.0151	40.0	1760	0.9856
0.9907	41.0	1804	0.9843
1.0147	42.0	1848	0.9803
1.001	43.0	1892	0.9777
1.0009	44.0	1936	0.9735
0.9881	45.0	1980	0.9731
0.9973	46.0	2024	0.9761
0.9982	47.0	2068	0.9888
0.9826	48.0	2112	1.0006
0.9739	49.0	2156	0.9766
0.9659	50.0	2200	0.9525
0.9534	51.0	2244	0.9400
0.959	52.0	2288	0.9553
0.9492	53.0	2332	0.9308
0.9629	54.0	2376	0.9325
0.9532	55.0	2420	0.9288
0.9586	56.0	2464	0.9233
0.9511	57.0	2508	0.9228
0.9456	58.0	2552	0.9178
0.937	59.0	2596	0.9140
0.9415	60.0	2640	0.9332
0.9364	61.0	2684	0.9073
0.9304	62.0	2728	0.9112
0.9418	63.0	2772	0.9073
0.9423	64.0	2816	0.9079
0.9277	65.0	2860	0.9062
0.9274	66.0	2904	0.8999
0.9266	67.0	2948	0.8971
0.9231	68.0	2992	0.9003
0.9174	69.0	3036	0.8994
0.9036	70.0	3080	0.8986
0.9112	71.0	3124	0.8925
0.8929	72.0	3168	0.8866
0.9069	73.0	3212	0.8840
0.8922	74.0	3256	0.8818
0.9079	75.0	3300	0.8821
0.8941	76.0	3344	0.8780
0.8952	77.0	3388	0.8824
0.8881	78.0	3432	0.8724
0.884	79.0	3476	0.8684
0.8761	80.0	3520	0.8715
0.8952	81.0	3564	0.8706
0.8871	82.0	3608	0.8654
0.8772	83.0	3652	0.8583
0.8745	84.0	3696	0.8570
0.8683	85.0	3740	0.8490
0.8698	86.0	3784	0.8500
0.8562	87.0	3828	0.8469
0.8636	88.0	3872	0.8465
0.8669	89.0	3916	0.8359
0.8422	90.0	3960	0.8418
0.8568	91.0	4004	0.8332
0.8628	92.0	4048	0.8338
0.8599	93.0	4092	0.8302
0.8471	94.0	4136	0.8235
0.8432	95.0	4180	0.8202
0.8389	96.0	4224	0.8159
0.8347	97.0	4268	0.8218
0.8353	98.0	4312	0.8141
0.8172	99.0	4356	0.8176
0.8303	100.0	4400	0.8078
0.8317	101.0	4444	0.8077
0.8203	102.0	4488	0.8103
0.8224	103.0	4532	0.8076
0.8174	104.0	4576	0.8023
0.8242	105.0	4620	0.7897
0.809	106.0	4664	0.7935
0.8014	107.0	4708	0.7881
0.817	108.0	4752	0.7815
0.7988	109.0	4796	0.7861
0.8003	110.0	4840	0.7716
0.7991	111.0	4884	0.7836
0.7851	112.0	4928	0.7722
0.7884	113.0	4972	0.7716
0.7831	114.0	5016	0.7643
0.7849	115.0	5060	0.7767
0.7846	116.0	5104	0.7602
0.7887	117.0	5148	0.7511
0.7683	118.0	5192	0.7480
0.7856	119.0	5236	0.7532
0.766	120.0	5280	0.7511
0.7663	121.0	5324	0.7490
0.7456	122.0	5368	0.7460
0.7672	123.0	5412	0.7464
0.7553	124.0	5456	0.7324
0.7543	125.0	5500	0.7296
0.7465	126.0	5544	0.7431
0.7525	127.0	5588	0.7310
0.7438	128.0	5632	0.7333
0.7521	129.0	5676	0.7218
0.7501	130.0	5720	0.7170
0.7485	131.0	5764	0.7214
0.7512	132.0	5808	0.7235
0.7554	133.0	5852	0.7140
0.7349	134.0	5896	0.7062
0.7542	135.0	5940	0.7095
0.7303	136.0	5984	0.7111
0.7163	137.0	6028	0.7004
0.7204	138.0	6072	0.7045
0.7091	139.0	6116	0.6918
0.719	140.0	6160	0.6976
0.726	141.0	6204	0.6885
0.7079	142.0	6248	0.6896
0.7043	143.0	6292	0.6966
0.7078	144.0	6336	0.6833
0.711	145.0	6380	0.6839
0.7014	146.0	6424	0.6685
0.7026	147.0	6468	0.6752
0.6927	148.0	6512	0.6802
0.6899	149.0	6556	0.6747
0.7059	150.0	6600	0.6733
0.6855	151.0	6644	0.6551
0.694	152.0	6688	0.6590
0.6896	153.0	6732	0.6568
0.6758	154.0	6776	0.6595
0.7058	155.0	6820	0.6506
0.6761	156.0	6864	0.6586
0.6837	157.0	6908	0.6526
0.6736	158.0	6952	0.6526
0.6738	159.0	6996	0.6434
0.685	160.0	7040	0.6382
0.664	161.0	7084	0.6374
0.6878	162.0	7128	0.6322
0.6552	163.0	7172	0.6338
0.6796	164.0	7216	0.6453
0.6712	165.0	7260	0.6284
0.6683	166.0	7304	0.6249
0.6577	167.0	7348	0.6359
0.6462	168.0	7392	0.6193
0.66	169.0	7436	0.6138
0.6476	170.0	7480	0.6224
0.6444	171.0	7524	0.6195
0.6478	172.0	7568	0.6136
0.6332	173.0	7612	0.5981
0.6456	174.0	7656	0.6004
0.6302	175.0	7700	0.6060
0.6337	176.0	7744	0.6024
0.6282	177.0	7788	0.5936
0.616	178.0	7832	0.5942
0.6324	179.0	7876	0.6038
0.6331	180.0	7920	0.5939
0.627	181.0	7964	0.5881
0.6313	182.0	8008	0.5874
0.626	183.0	8052	0.5868
0.6215	184.0	8096	0.5789
0.6138	185.0	8140	0.5830
0.6235	186.0	8184	0.5900
0.61	187.0	8228	0.5920
0.6218	188.0	8272	0.5830
0.6265	189.0	8316	0.5706
0.6126	190.0	8360	0.5776
0.608	191.0	8404	0.5738
0.6143	192.0	8448	0.5737
0.6065	193.0	8492	0.5714
0.6213	194.0	8536	0.5657
0.6004	195.0	8580	0.5660
0.6229	196.0	8624	0.5646
0.6073	197.0	8668	0.5704
0.6048	198.0	8712	0.5696
0.6008	199.0	8756	0.5619
0.6157	200.0	8800	0.5597

Framework versions

• Transformers 4.34.0
• Pytorch 2.1.0+cu121
• Datasets 2.14.5
• Tokenizers 0.14.1