ellis-v2-emotion-leadership

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7460
• Accuracy: 0.9411

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 70

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3621	1.0	1109	0.3042	0.8964
0.257	2.0	2218	0.2566	0.9259
0.1991	3.0	3327	0.2492	0.9274
0.1599	4.0	4436	0.2860	0.9320
0.1335	5.0	5545	0.2966	0.9299
0.1082	6.0	6654	0.3682	0.9274
0.0805	7.0	7763	0.3384	0.9381
0.056	8.0	8872	0.4321	0.9325
0.0391	9.0	9981	0.4476	0.9264
0.0431	10.0	11090	0.5036	0.9254
0.037	11.0	12199	0.4724	0.9315
0.032	12.0	13308	0.4975	0.9381
0.0248	13.0	14417	0.5242	0.9294
0.0194	14.0	15526	0.5792	0.9305
0.0309	15.0	16635	0.5574	0.9315
0.0309	16.0	17744	0.5071	0.9355
0.0223	17.0	18853	0.5156	0.9355
0.0235	18.0	19962	0.5363	0.9371
0.014	19.0	21071	0.6050	0.9294
0.0227	20.0	22180	0.5531	0.9371
0.0133	21.0	23289	0.6171	0.9355
0.0215	22.0	24398	0.5730	0.9320
0.0143	23.0	25507	0.5958	0.9330
0.0139	24.0	26616	0.5780	0.9335
0.0104	25.0	27725	0.6212	0.9315
0.0125	26.0	28834	0.6119	0.9335
0.007	27.0	29943	0.6179	0.9360
0.016	28.0	31052	0.6422	0.9355
0.0128	29.0	32161	0.6028	0.9360
0.007	30.0	33270	0.6751	0.9320
0.0109	31.0	34379	0.6579	0.9371
0.0055	32.0	35488	0.7140	0.9305
0.0116	33.0	36597	0.6488	0.9360
0.0138	34.0	37706	0.6029	0.9345
0.0095	35.0	38815	0.6393	0.9355
0.0041	36.0	39924	0.6387	0.9355
0.0063	37.0	41033	0.6304	0.9371
0.0037	38.0	42142	0.6349	0.9391
0.0077	39.0	43251	0.6230	0.9406
0.0027	40.0	44360	0.6546	0.9426
0.0022	41.0	45469	0.7147	0.9350
0.0054	42.0	46578	0.7450	0.9310
0.006	43.0	47687	0.6921	0.9360
0.0035	44.0	48796	0.6667	0.9376
0.0078	45.0	49905	0.6562	0.9371
0.0038	46.0	51014	0.6589	0.9376
0.0032	47.0	52123	0.6429	0.9371
0.0002	48.0	53232	0.6616	0.9386
0.0022	49.0	54341	0.6737	0.9416
0.0	50.0	55450	0.6911	0.9421
0.0004	51.0	56559	0.7703	0.9335
0.0047	52.0	57668	0.7535	0.9345
0.0003	53.0	58777	0.7973	0.9284
0.0026	54.0	59886	0.7266	0.9376
0.0047	55.0	60995	0.7328	0.9340
0.0	56.0	62104	0.7422	0.9371
0.0006	57.0	63213	0.7275	0.9371
0.0008	58.0	64322	0.7095	0.9396
0.0009	59.0	65431	0.7112	0.9401
0.0017	60.0	66540	0.6923	0.9421
0.0022	61.0	67649	0.7383	0.9376
0.0	62.0	68758	0.7314	0.9391
0.0004	63.0	69867	0.7433	0.9381
0.0	64.0	70976	0.7410	0.9386
0.0	65.0	72085	0.7519	0.9386
0.0003	66.0	73194	0.7459	0.9406
0.0004	67.0	74303	0.7366	0.9401
0.0	68.0	75412	0.7318	0.9411
0.0	69.0	76521	0.7430	0.9411
0.0	70.0	77630	0.7460	0.9411

Framework versions

• Transformers 4.40.0
• Pytorch 2.1.0
• Datasets 2.19.0
• Tokenizers 0.19.1