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gonzalezrostani/
my_awesome_wnut_JPr

Name: my_awesome_wnut_JPr
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

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my_awesome_wnut_JPr

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: 0.1079
Precision: 0.845
Recall: 0.8989
F1: 0.8711
Accuracy: 0.9872

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	0.0717	0.7583	0.8511	0.8020	0.9775
No log	2.0	92	0.0347	0.8301	0.9096	0.8680	0.9866
No log	3.0	138	0.0390	0.8164	0.8989	0.8557	0.9854
No log	4.0	184	0.0377	0.8458	0.9043	0.8740	0.9869
No log	5.0	230	0.0486	0.8454	0.9309	0.8861	0.9881
No log	6.0	276	0.0435	0.8492	0.8989	0.8734	0.9878
No log	7.0	322	0.0500	0.8458	0.9043	0.8740	0.9872
No log	8.0	368	0.0586	0.8447	0.9255	0.8832	0.9878
No log	9.0	414	0.0573	0.8384	0.8830	0.8601	0.9863
No log	10.0	460	0.0611	0.8482	0.8617	0.8549	0.9860
0.0327	11.0	506	0.0713	0.8238	0.9202	0.8693	0.9863
0.0327	12.0	552	0.0733	0.8416	0.9043	0.8718	0.9869
0.0327	13.0	598	0.0641	0.8653	0.8883	0.8766	0.9875
0.0327	14.0	644	0.0728	0.8301	0.9096	0.8680	0.9866
0.0327	15.0	690	0.0698	0.8507	0.9096	0.8792	0.9881
0.0327	16.0	736	0.0778	0.8390	0.9149	0.8753	0.9872
0.0327	17.0	782	0.0796	0.8520	0.8883	0.8698	0.9866
0.0327	18.0	828	0.0824	0.8462	0.8777	0.8616	0.9860
0.0327	19.0	874	0.0775	0.8571	0.8936	0.875	0.9869
0.0327	20.0	920	0.0828	0.8439	0.9202	0.8804	0.9875
0.0327	21.0	966	0.0794	0.8520	0.8883	0.8698	0.9860
0.0015	22.0	1012	0.0707	0.8492	0.8989	0.8734	0.9866
0.0015	23.0	1058	0.0797	0.8398	0.9202	0.8782	0.9872
0.0015	24.0	1104	0.0780	0.8473	0.9149	0.8798	0.9875
0.0015	25.0	1150	0.0805	0.8480	0.9202	0.8827	0.9875
0.0015	26.0	1196	0.0797	0.8480	0.9202	0.8827	0.9881
0.0015	27.0	1242	0.0812	0.8488	0.9255	0.8855	0.9881
0.0015	28.0	1288	0.0798	0.8535	0.8989	0.8756	0.9869
0.0015	29.0	1334	0.0851	0.8488	0.9255	0.8855	0.9878
0.0015	30.0	1380	0.0806	0.85	0.9043	0.8763	0.9875
0.0015	31.0	1426	0.0919	0.8325	0.9255	0.8766	0.9866
0.0015	32.0	1472	0.0897	0.8406	0.9255	0.8810	0.9875
0.0005	33.0	1518	0.0795	0.8621	0.9309	0.8951	0.9884
0.0005	34.0	1564	0.0831	0.8439	0.9202	0.8804	0.9872
0.0005	35.0	1610	0.0842	0.8431	0.9149	0.8776	0.9869
0.0005	36.0	1656	0.0854	0.8564	0.9202	0.8872	0.9878
0.0005	37.0	1702	0.0966	0.8431	0.9149	0.8776	0.9872
0.0005	38.0	1748	0.0971	0.8458	0.9043	0.8740	0.9872
0.0005	39.0	1794	0.0931	0.8480	0.9202	0.8827	0.9878
0.0005	40.0	1840	0.0998	0.8365	0.9255	0.8788	0.9872
0.0005	41.0	1886	0.0946	0.8458	0.9043	0.8740	0.9872
0.0005	42.0	1932	0.0933	0.8431	0.9149	0.8776	0.9872
0.0005	43.0	1978	0.0921	0.8431	0.9149	0.8776	0.9872
0.0006	44.0	2024	0.0907	0.8513	0.8830	0.8668	0.9863
0.0006	45.0	2070	0.0962	0.8410	0.8723	0.8564	0.9860
0.0006	46.0	2116	0.0992	0.8333	0.9043	0.8673	0.9860
0.0006	47.0	2162	0.0924	0.8586	0.9043	0.8808	0.9881
0.0006	48.0	2208	0.1051	0.8309	0.9149	0.8709	0.9866
0.0006	49.0	2254	0.1047	0.8392	0.8883	0.8630	0.9866
0.0006	50.0	2300	0.1053	0.8392	0.8883	0.8630	0.9866
0.0006	51.0	2346	0.1055	0.8252	0.9043	0.8629	0.9860
0.0006	52.0	2392	0.1053	0.8244	0.8989	0.8601	0.9860
0.0006	53.0	2438	0.1064	0.8284	0.8989	0.8622	0.9860
0.0006	54.0	2484	0.1070	0.8284	0.8989	0.8622	0.9860
0.0004	55.0	2530	0.1074	0.8366	0.8989	0.8667	0.9866
0.0004	56.0	2576	0.1083	0.8408	0.8989	0.8689	0.9869
0.0004	57.0	2622	0.1085	0.8325	0.8989	0.8645	0.9863
0.0004	58.0	2668	0.1090	0.8325	0.8989	0.8645	0.9866
0.0004	59.0	2714	0.1088	0.84	0.8936	0.8660	0.9866
0.0004	60.0	2760	0.1089	0.84	0.8936	0.8660	0.9866
0.0004	61.0	2806	0.1082	0.8442	0.8936	0.8682	0.9869
0.0004	62.0	2852	0.1088	0.8442	0.8936	0.8682	0.9869
0.0004	63.0	2898	0.1042	0.8557	0.8830	0.8691	0.9872
0.0004	64.0	2944	0.1029	0.8408	0.8989	0.8689	0.9869
0.0004	65.0	2990	0.1035	0.8374	0.9043	0.8696	0.9869
0.0003	66.0	3036	0.1045	0.8374	0.9043	0.8696	0.9869
0.0003	67.0	3082	0.1046	0.8374	0.9043	0.8696	0.9869
0.0003	68.0	3128	0.1059	0.8374	0.9043	0.8696	0.9869
0.0003	69.0	3174	0.1070	0.8366	0.8989	0.8667	0.9866
0.0003	70.0	3220	0.1075	0.8366	0.8989	0.8667	0.9866
0.0003	71.0	3266	0.1096	0.8477	0.8883	0.8675	0.9872
0.0003	72.0	3312	0.1104	0.8485	0.8936	0.8705	0.9875
0.0003	73.0	3358	0.1109	0.8485	0.8936	0.8705	0.9875
0.0003	74.0	3404	0.1097	0.8341	0.9096	0.8702	0.9863
0.0003	75.0	3450	0.1094	0.8325	0.8989	0.8645	0.9863
0.0003	76.0	3496	0.1098	0.8325	0.8989	0.8645	0.9863
0.0003	77.0	3542	0.1060	0.8477	0.8883	0.8675	0.9872
0.0003	78.0	3588	0.1065	0.8366	0.8989	0.8667	0.9866
0.0003	79.0	3634	0.1078	0.8374	0.9043	0.8696	0.9863
0.0003	80.0	3680	0.1077	0.8374	0.9043	0.8696	0.9863
0.0003	81.0	3726	0.1069	0.8366	0.8989	0.8667	0.9866
0.0003	82.0	3772	0.1075	0.8366	0.8989	0.8667	0.9866
0.0003	83.0	3818	0.1070	0.8408	0.8989	0.8689	0.9869
0.0003	84.0	3864	0.1070	0.8408	0.8989	0.8689	0.9869
0.0003	85.0	3910	0.1074	0.8408	0.8989	0.8689	0.9869
0.0003	86.0	3956	0.1074	0.8408	0.8989	0.8689	0.9869
0.0003	87.0	4002	0.1092	0.8374	0.9043	0.8696	0.9863
0.0003	88.0	4048	0.1084	0.8374	0.9043	0.8696	0.9866
0.0003	89.0	4094	0.1085	0.8374	0.9043	0.8696	0.9866
0.0003	90.0	4140	0.1085	0.8374	0.9043	0.8696	0.9866
0.0003	91.0	4186	0.1087	0.8374	0.9043	0.8696	0.9866
0.0003	92.0	4232	0.1088	0.8374	0.9043	0.8696	0.9866
0.0003	93.0	4278	0.1088	0.8374	0.9043	0.8696	0.9866
0.0003	94.0	4324	0.1090	0.8374	0.9043	0.8696	0.9866
0.0003	95.0	4370	0.1075	0.8492	0.8989	0.8734	0.9875
0.0003	96.0	4416	0.1075	0.8492	0.8989	0.8734	0.9875
0.0003	97.0	4462	0.1076	0.8492	0.8989	0.8734	0.9875
0.0003	98.0	4508	0.1077	0.8492	0.8989	0.8734	0.9875
0.0003	99.0	4554	0.1078	0.8492	0.8989	0.8734	0.9875
0.0003	100.0	4600	0.1079	0.845	0.8989	0.8711	0.9872

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.2.2+cu118
Datasets 2.18.0
Tokenizers 0.15.2

gonzalezrostani/my_awesome_wnut_JPr

my_awesome_wnut_JPr

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gonzalezrostani/
my_awesome_wnut_JPr