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gonzalezrostani/
my_awesome_wnut_JGTt

Name: my_awesome_wnut_JGTt
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

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my_awesome_wnut_JGTt

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

Loss: 0.1104
Precision: 0.4118
Recall: 0.2917
F1: 0.3415
Accuracy: 0.9881

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	46	0.0614	0.0	0.0	0.0	0.9884
No log	2.0	92	0.0510	0.0	0.0	0.0	0.9884
No log	3.0	138	0.0586	0.5	0.0417	0.0769	0.9884
No log	4.0	184	0.0615	0.25	0.2083	0.2273	0.9863
No log	5.0	230	0.0665	0.6	0.25	0.3529	0.9891
No log	6.0	276	0.0631	0.4444	0.3333	0.3810	0.9884
No log	7.0	322	0.0668	0.35	0.2917	0.3182	0.9872
No log	8.0	368	0.0762	0.3846	0.2083	0.2703	0.9875
No log	9.0	414	0.0875	0.4545	0.2083	0.2857	0.9881
No log	10.0	460	0.0866	0.4545	0.2083	0.2857	0.9884
0.0196	11.0	506	0.0866	0.375	0.25	0.3	0.9872
0.0196	12.0	552	0.0899	0.4	0.25	0.3077	0.9875
0.0196	13.0	598	0.0907	0.375	0.25	0.3	0.9872
0.0196	14.0	644	0.0933	0.4286	0.25	0.3158	0.9881
0.0196	15.0	690	0.0952	0.4286	0.25	0.3158	0.9881
0.0196	16.0	736	0.0972	0.4286	0.25	0.3158	0.9881
0.0196	17.0	782	0.0989	0.4286	0.25	0.3158	0.9881
0.0196	18.0	828	0.1037	0.4545	0.2083	0.2857	0.9884
0.0196	19.0	874	0.0998	0.4118	0.2917	0.3415	0.9875
0.0196	20.0	920	0.1026	0.5556	0.2083	0.3030	0.9887
0.0196	21.0	966	0.1064	0.5	0.125	0.2	0.9887
0.0004	22.0	1012	0.0950	0.4	0.25	0.3077	0.9881
0.0004	23.0	1058	0.0916	0.5714	0.3333	0.4211	0.9897
0.0004	24.0	1104	0.0944	0.5455	0.25	0.3429	0.9894
0.0004	25.0	1150	0.0964	0.5	0.25	0.3333	0.9891
0.0004	26.0	1196	0.1129	0.4286	0.125	0.1935	0.9884
0.0004	27.0	1242	0.1003	0.375	0.25	0.3	0.9878
0.0004	28.0	1288	0.1017	0.3333	0.2083	0.2564	0.9875
0.0004	29.0	1334	0.1048	0.3125	0.2083	0.25	0.9875
0.0004	30.0	1380	0.1030	0.3	0.25	0.2727	0.9863
0.0004	31.0	1426	0.1038	0.35	0.2917	0.3182	0.9872
0.0004	32.0	1472	0.1055	0.4286	0.25	0.3158	0.9881
0.0003	33.0	1518	0.1068	0.4615	0.25	0.3243	0.9887
0.0003	34.0	1564	0.1084	0.3529	0.25	0.2927	0.9878
0.0003	35.0	1610	0.1088	0.3846	0.2083	0.2703	0.9881
0.0003	36.0	1656	0.1182	0.5714	0.1667	0.2581	0.9891
0.0003	37.0	1702	0.1145	0.4545	0.2083	0.2857	0.9881
0.0003	38.0	1748	0.1156	0.4545	0.2083	0.2857	0.9881
0.0003	39.0	1794	0.1150	0.4167	0.2083	0.2778	0.9878
0.0003	40.0	1840	0.1161	0.4167	0.2083	0.2778	0.9878
0.0003	41.0	1886	0.1118	0.4615	0.25	0.3243	0.9881
0.0003	42.0	1932	0.1119	0.4545	0.2083	0.2857	0.9881
0.0003	43.0	1978	0.1113	0.5	0.25	0.3333	0.9884
0.0002	44.0	2024	0.1182	0.6	0.125	0.2069	0.9891
0.0002	45.0	2070	0.0972	0.3889	0.2917	0.3333	0.9887
0.0002	46.0	2116	0.1022	0.3	0.375	0.3333	0.9857
0.0002	47.0	2162	0.0924	0.4091	0.375	0.3913	0.9881
0.0002	48.0	2208	0.1000	0.5	0.25	0.3333	0.9884
0.0002	49.0	2254	0.0971	0.4286	0.375	0.4000	0.9881
0.0002	50.0	2300	0.0975	0.3529	0.25	0.2927	0.9875
0.0002	51.0	2346	0.1049	0.4	0.1667	0.2353	0.9881
0.0002	52.0	2392	0.1052	0.3636	0.1667	0.2286	0.9875
0.0002	53.0	2438	0.1054	0.3571	0.2083	0.2632	0.9872
0.0002	54.0	2484	0.1069	0.3636	0.1667	0.2286	0.9875
0.0007	55.0	2530	0.1080	0.3636	0.1667	0.2286	0.9875
0.0007	56.0	2576	0.1184	0.5714	0.1667	0.2581	0.9891
0.0007	57.0	2622	0.1029	0.4545	0.4167	0.4348	0.9891
0.0007	58.0	2668	0.1033	0.4286	0.375	0.4000	0.9887
0.0007	59.0	2714	0.1039	0.4	0.3333	0.3636	0.9884
0.0007	60.0	2760	0.1047	0.3889	0.2917	0.3333	0.9884
0.0007	61.0	2806	0.1041	0.4167	0.4167	0.4167	0.9891
0.0007	62.0	2852	0.1045	0.4286	0.375	0.4000	0.9887
0.0007	63.0	2898	0.1046	0.4286	0.375	0.4000	0.9887
0.0007	64.0	2944	0.1047	0.4211	0.3333	0.3721	0.9887
0.0007	65.0	2990	0.1078	0.4348	0.4167	0.4255	0.9887
0.0002	66.0	3036	0.1054	0.4118	0.2917	0.3415	0.9881
0.0002	67.0	3082	0.1054	0.4118	0.2917	0.3415	0.9881
0.0002	68.0	3128	0.1057	0.4118	0.2917	0.3415	0.9881
0.0002	69.0	3174	0.1060	0.4118	0.2917	0.3415	0.9881
0.0002	70.0	3220	0.1062	0.4118	0.2917	0.3415	0.9881
0.0002	71.0	3266	0.1066	0.4118	0.2917	0.3415	0.9881
0.0002	72.0	3312	0.1068	0.4118	0.2917	0.3415	0.9881
0.0002	73.0	3358	0.1071	0.4118	0.2917	0.3415	0.9881
0.0002	74.0	3404	0.1071	0.4118	0.2917	0.3415	0.9881
0.0002	75.0	3450	0.1076	0.4118	0.2917	0.3415	0.9881
0.0002	76.0	3496	0.1077	0.4118	0.2917	0.3415	0.9881
0.0001	77.0	3542	0.1077	0.4118	0.2917	0.3415	0.9881
0.0001	78.0	3588	0.1076	0.4118	0.2917	0.3415	0.9881
0.0001	79.0	3634	0.1079	0.4118	0.2917	0.3415	0.9881
0.0001	80.0	3680	0.1082	0.4118	0.2917	0.3415	0.9881
0.0001	81.0	3726	0.1084	0.4118	0.2917	0.3415	0.9881
0.0001	82.0	3772	0.1085	0.4118	0.2917	0.3415	0.9881
0.0001	83.0	3818	0.1090	0.4118	0.2917	0.3415	0.9881
0.0001	84.0	3864	0.1091	0.4118	0.2917	0.3415	0.9881
0.0001	85.0	3910	0.1091	0.4118	0.2917	0.3415	0.9881
0.0001	86.0	3956	0.1094	0.4118	0.2917	0.3415	0.9881
0.0001	87.0	4002	0.1094	0.4118	0.2917	0.3415	0.9881
0.0001	88.0	4048	0.1095	0.4118	0.2917	0.3415	0.9881
0.0001	89.0	4094	0.1097	0.4118	0.2917	0.3415	0.9881
0.0001	90.0	4140	0.1098	0.4118	0.2917	0.3415	0.9881
0.0001	91.0	4186	0.1099	0.4118	0.2917	0.3415	0.9881
0.0001	92.0	4232	0.1100	0.4118	0.2917	0.3415	0.9881
0.0001	93.0	4278	0.1101	0.4118	0.2917	0.3415	0.9881
0.0001	94.0	4324	0.1102	0.4118	0.2917	0.3415	0.9881
0.0001	95.0	4370	0.1102	0.4118	0.2917	0.3415	0.9881
0.0001	96.0	4416	0.1103	0.4118	0.2917	0.3415	0.9881
0.0001	97.0	4462	0.1103	0.4118	0.2917	0.3415	0.9881
0.0001	98.0	4508	0.1103	0.4118	0.2917	0.3415	0.9881
0.0001	99.0	4554	0.1104	0.4118	0.2917	0.3415	0.9881
0.0001	100.0	4600	0.1104	0.4118	0.2917	0.3415	0.9881

Framework versions

Transformers 4.39.1
Pytorch 2.2.2+cu118
Datasets 2.18.0
Tokenizers 0.15.2

gonzalezrostani/my_awesome_wnut_JGTt

my_awesome_wnut_JGTt

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gonzalezrostani/
my_awesome_wnut_JGTt