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: 0.0882
Precision: 0.7944
Recall: 0.8333
F1: 0.8134
Accuracy: 0.9897

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	0.0399	0.7826	0.8824	0.8295	0.9900
No log	2.0	92	0.0316	0.7830	0.8137	0.7981	0.9884
No log	3.0	138	0.0313	0.7833	0.9216	0.8468	0.9915
No log	4.0	184	0.0290	0.8	0.8627	0.8302	0.9912
No log	5.0	230	0.0340	0.8	0.8235	0.8116	0.9900
No log	6.0	276	0.0385	0.7982	0.8922	0.8426	0.9912
No log	7.0	322	0.0422	0.7966	0.9216	0.8545	0.9918
No log	8.0	368	0.0442	0.8018	0.8725	0.8357	0.9912
No log	9.0	414	0.0588	0.8022	0.7157	0.7565	0.9866
No log	10.0	460	0.0457	0.7857	0.8627	0.8224	0.9903
0.0246	11.0	506	0.0579	0.7982	0.8529	0.8246	0.9903
0.0246	12.0	552	0.0622	0.7857	0.8627	0.8224	0.9900
0.0246	13.0	598	0.0613	0.7876	0.8725	0.8279	0.9903
0.0246	14.0	644	0.0642	0.7857	0.8627	0.8224	0.9900
0.0246	15.0	690	0.0660	0.8	0.8627	0.8302	0.9906
0.0246	16.0	736	0.0674	0.7857	0.8627	0.8224	0.9900
0.0246	17.0	782	0.0697	0.8073	0.8627	0.8341	0.9909
0.0246	18.0	828	0.0714	0.7944	0.8333	0.8134	0.9897
0.0246	19.0	874	0.0700	0.7981	0.8137	0.8058	0.9894
0.0246	20.0	920	0.0655	0.7925	0.8235	0.8077	0.9894
0.0246	21.0	966	0.0659	0.7863	0.9020	0.8402	0.9912
0.0005	22.0	1012	0.0760	0.7961	0.8039	0.8	0.9891
0.0005	23.0	1058	0.0741	0.7944	0.8333	0.8134	0.9897
0.0005	24.0	1104	0.0722	0.7788	0.8627	0.8186	0.9897
0.0005	25.0	1150	0.0832	0.8061	0.7745	0.79	0.9887
0.0005	26.0	1196	0.0758	0.7905	0.8137	0.8019	0.9891
0.0005	27.0	1242	0.0635	0.7850	0.8235	0.8038	0.9891
0.0005	28.0	1288	0.0650	0.7928	0.8627	0.8263	0.9903
0.0005	29.0	1334	0.0718	0.7928	0.8627	0.8263	0.9903
0.0005	30.0	1380	0.0695	0.7881	0.9118	0.8455	0.9912
0.0005	31.0	1426	0.0679	0.7966	0.9216	0.8545	0.9915
0.0005	32.0	1472	0.0702	0.8	0.8627	0.8302	0.9906
0.0004	33.0	1518	0.0697	0.7833	0.9216	0.8468	0.9915
0.0004	34.0	1564	0.0690	0.8	0.9020	0.8479	0.9912
0.0004	35.0	1610	0.0693	0.7982	0.8529	0.8246	0.9903
0.0004	36.0	1656	0.0689	0.8018	0.8725	0.8357	0.9909
0.0004	37.0	1702	0.0695	0.8018	0.8725	0.8357	0.9909
0.0004	38.0	1748	0.0696	0.8036	0.8824	0.8411	0.9909
0.0004	39.0	1794	0.0702	0.8053	0.8922	0.8465	0.9912
0.0004	40.0	1840	0.0756	0.8037	0.8431	0.8230	0.9903
0.0004	41.0	1886	0.0738	0.7946	0.8725	0.8318	0.9906
0.0004	42.0	1932	0.0730	0.7966	0.9216	0.8545	0.9921
0.0004	43.0	1978	0.0740	0.8034	0.9216	0.8584	0.9918
0.0002	44.0	2024	0.0743	0.8034	0.9216	0.8584	0.9918
0.0002	45.0	2070	0.0751	0.8034	0.9216	0.8584	0.9918
0.0002	46.0	2116	0.0749	0.8034	0.9216	0.8584	0.9918
0.0002	47.0	2162	0.0752	0.7931	0.9020	0.8440	0.9912
0.0002	48.0	2208	0.0757	0.7913	0.8922	0.8387	0.9909
0.0002	49.0	2254	0.0760	0.7913	0.8922	0.8387	0.9909
0.0002	50.0	2300	0.0743	0.7965	0.8824	0.8372	0.9909
0.0002	51.0	2346	0.0745	0.7965	0.8824	0.8372	0.9909
0.0002	52.0	2392	0.0757	0.8018	0.8725	0.8357	0.9909
0.0002	53.0	2438	0.0763	0.8	0.8627	0.8302	0.9906
0.0002	54.0	2484	0.0762	0.8018	0.8725	0.8357	0.9909
0.0001	55.0	2530	0.0764	0.8018	0.8725	0.8357	0.9909
0.0001	56.0	2576	0.0833	0.7881	0.9118	0.8455	0.9912
0.0001	57.0	2622	0.0770	0.7881	0.9118	0.8455	0.9915
0.0001	58.0	2668	0.0713	0.7965	0.8824	0.8372	0.9906
0.0001	59.0	2714	0.0753	0.7876	0.8725	0.8279	0.9903
0.0001	60.0	2760	0.0750	0.7931	0.9020	0.8440	0.9912
0.0001	61.0	2806	0.0768	0.7838	0.8529	0.8169	0.9897
0.0001	62.0	2852	0.0758	0.7982	0.8922	0.8426	0.9909
0.0001	63.0	2898	0.0766	0.7982	0.8922	0.8426	0.9909
0.0001	64.0	2944	0.0773	0.7895	0.8824	0.8333	0.9906
0.0001	65.0	2990	0.0779	0.7895	0.8824	0.8333	0.9906
0.0004	66.0	3036	0.0783	0.7895	0.8824	0.8333	0.9906
0.0004	67.0	3082	0.0790	0.7895	0.8824	0.8333	0.9906
0.0004	68.0	3128	0.0795	0.7895	0.8824	0.8333	0.9906
0.0004	69.0	3174	0.0800	0.7895	0.8824	0.8333	0.9906
0.0004	70.0	3220	0.0806	0.7895	0.8824	0.8333	0.9906
0.0004	71.0	3266	0.0810	0.7895	0.8824	0.8333	0.9906
0.0004	72.0	3312	0.0812	0.7895	0.8824	0.8333	0.9906
0.0004	73.0	3358	0.0815	0.7895	0.8824	0.8333	0.9906
0.0004	74.0	3404	0.0818	0.7895	0.8824	0.8333	0.9906
0.0004	75.0	3450	0.0821	0.7895	0.8824	0.8333	0.9906
0.0004	76.0	3496	0.0864	0.8	0.8235	0.8116	0.9897
0.0001	77.0	3542	0.0847	0.7944	0.8333	0.8134	0.9897
0.0001	78.0	3588	0.0849	0.7944	0.8333	0.8134	0.9897
0.0001	79.0	3634	0.0852	0.7944	0.8333	0.8134	0.9897
0.0001	80.0	3680	0.0854	0.7890	0.8431	0.8152	0.9897
0.0001	81.0	3726	0.0855	0.7890	0.8431	0.8152	0.9897
0.0001	82.0	3772	0.0837	0.7857	0.8627	0.8224	0.9900
0.0001	83.0	3818	0.0838	0.7857	0.8627	0.8224	0.9900
0.0001	84.0	3864	0.0840	0.7857	0.8627	0.8224	0.9900
0.0001	85.0	3910	0.0842	0.7946	0.8725	0.8318	0.9903
0.0001	86.0	3956	0.0843	0.7857	0.8627	0.8224	0.9900
0.0001	87.0	4002	0.0845	0.7857	0.8627	0.8224	0.9900
0.0001	88.0	4048	0.0845	0.7857	0.8627	0.8224	0.9900
0.0001	89.0	4094	0.0846	0.7946	0.8725	0.8318	0.9903
0.0001	90.0	4140	0.0847	0.7946	0.8725	0.8318	0.9903
0.0001	91.0	4186	0.0847	0.7857	0.8627	0.8224	0.9900
0.0001	92.0	4232	0.0848	0.7857	0.8627	0.8224	0.9900
0.0001	93.0	4278	0.0894	0.8	0.8235	0.8116	0.9897
0.0001	94.0	4324	0.0895	0.8	0.8235	0.8116	0.9897
0.0001	95.0	4370	0.0895	0.8	0.8235	0.8116	0.9897
0.0001	96.0	4416	0.0895	0.8	0.8235	0.8116	0.9897
0.0001	97.0	4462	0.0894	0.8	0.8235	0.8116	0.9897
0.0001	98.0	4508	0.0893	0.8	0.8235	0.8116	0.9897
0.0001	99.0	4554	0.0882	0.7944	0.8333	0.8134	0.9897
0.0001	100.0	4600	0.0882	0.7944	0.8333	0.8134	0.9897

Framework versions

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

gonzalezrostani/my_awesome_wnut_JHs

my_awesome_wnut_JHs

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gonzalezrostani/
my_awesome_wnut_JHs