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gonzalezrostani/
my_awesome_wnut_SAprotection

Name: my_awesome_wnut_SAprotection
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

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my_awesome_wnut_SAprotection

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: 0.6175
Precision: 0.4476
Recall: 0.3232
F1: 0.3754
Accuracy: 0.9352

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	0.1989	0.0	0.0	0.0	0.9398
No log	2.0	92	0.1872	0.5943	0.3182	0.4145	0.9459
No log	3.0	138	0.1829	0.6220	0.2576	0.3643	0.9459
No log	4.0	184	0.2139	0.5086	0.2980	0.3758	0.9404
No log	5.0	230	0.2380	0.4479	0.3687	0.4044	0.9346
No log	6.0	276	0.2839	0.4379	0.3384	0.3818	0.9340
No log	7.0	322	0.3035	0.4514	0.3283	0.3801	0.9355
No log	8.0	368	0.3392	0.4380	0.3030	0.3582	0.9346
No log	9.0	414	0.3538	0.3946	0.3687	0.3812	0.9279
No log	10.0	460	0.3880	0.4277	0.3737	0.3989	0.9322
0.0858	11.0	506	0.3886	0.4574	0.2980	0.3609	0.9364
0.0858	12.0	552	0.4140	0.4324	0.3232	0.3699	0.9337
0.0858	13.0	598	0.4233	0.4379	0.3384	0.3818	0.9340
0.0858	14.0	644	0.4345	0.4195	0.3687	0.3925	0.9313
0.0858	15.0	690	0.4458	0.4821	0.2727	0.3484	0.9386
0.0858	16.0	736	0.4571	0.4889	0.3333	0.3964	0.9389
0.0858	17.0	782	0.4697	0.4825	0.2778	0.3526	0.9386
0.0858	18.0	828	0.4662	0.4583	0.3333	0.3860	0.9361
0.0858	19.0	874	0.4710	0.4242	0.3535	0.3857	0.9322
0.0858	20.0	920	0.4799	0.4303	0.3586	0.3912	0.9328
0.0858	21.0	966	0.5134	0.5050	0.2576	0.3411	0.9401
0.0063	22.0	1012	0.5115	0.42	0.3182	0.3621	0.9325
0.0063	23.0	1058	0.5055	0.4848	0.2424	0.3232	0.9389
0.0063	24.0	1104	0.4961	0.4045	0.3636	0.3830	0.9294
0.0063	25.0	1150	0.4851	0.4375	0.3535	0.3911	0.9337
0.0063	26.0	1196	0.4866	0.5143	0.2727	0.3564	0.9407
0.0063	27.0	1242	0.4907	0.5093	0.2778	0.3595	0.9404
0.0063	28.0	1288	0.4813	0.4422	0.3283	0.3768	0.9346
0.0063	29.0	1334	0.5007	0.48	0.3030	0.3715	0.9383
0.0063	30.0	1380	0.5063	0.4493	0.3131	0.3690	0.9355
0.0063	31.0	1426	0.5083	0.4516	0.3535	0.3966	0.9352
0.0063	32.0	1472	0.5191	0.4914	0.2879	0.3631	0.9392
0.0037	33.0	1518	0.5252	0.4626	0.3434	0.3942	0.9364
0.0037	34.0	1564	0.5330	0.4715	0.2929	0.3614	0.9377
0.0037	35.0	1610	0.5340	0.4340	0.3485	0.3866	0.9334
0.0037	36.0	1656	0.5209	0.4729	0.3081	0.3731	0.9377
0.0037	37.0	1702	0.5391	0.5049	0.2626	0.3455	0.9401
0.0037	38.0	1748	0.5311	0.4815	0.3283	0.3904	0.9383
0.0037	39.0	1794	0.5348	0.4497	0.3384	0.3862	0.9352
0.0037	40.0	1840	0.5452	0.4762	0.3030	0.3704	0.9380
0.0037	41.0	1886	0.5558	0.4948	0.2424	0.3254	0.9395
0.0037	42.0	1932	0.5495	0.4557	0.3636	0.4045	0.9355
0.0037	43.0	1978	0.5629	0.4403	0.2980	0.3554	0.9349
0.0027	44.0	2024	0.5628	0.4596	0.3737	0.4123	0.9358
0.0027	45.0	2070	0.5649	0.4923	0.3232	0.3902	0.9392
0.0027	46.0	2116	0.5694	0.4516	0.3535	0.3966	0.9352
0.0027	47.0	2162	0.5777	0.5	0.2576	0.3400	0.9398
0.0027	48.0	2208	0.5667	0.4681	0.3333	0.3894	0.9370
0.0027	49.0	2254	0.5727	0.4417	0.3636	0.3989	0.9340
0.0027	50.0	2300	0.5730	0.4697	0.3131	0.3758	0.9373
0.0027	51.0	2346	0.5726	0.4783	0.3333	0.3929	0.9380
0.0027	52.0	2392	0.5785	0.4911	0.2778	0.3548	0.9392
0.0027	53.0	2438	0.5763	0.4870	0.2828	0.3578	0.9389
0.0027	54.0	2484	0.5729	0.4610	0.3586	0.4034	0.9361
0.0022	55.0	2530	0.5759	0.4672	0.3232	0.3821	0.9370
0.0022	56.0	2576	0.5781	0.4631	0.3485	0.3977	0.9364
0.0022	57.0	2622	0.5806	0.4783	0.3333	0.3929	0.9380
0.0022	58.0	2668	0.5905	0.4444	0.3636	0.4000	0.9343
0.0022	59.0	2714	0.5896	0.4783	0.2778	0.3514	0.9383
0.0022	60.0	2760	0.5906	0.4552	0.3333	0.3848	0.9358
0.0022	61.0	2806	0.5916	0.4474	0.3434	0.3886	0.9349
0.0022	62.0	2852	0.5926	0.44	0.2778	0.3406	0.9352
0.0022	63.0	2898	0.6246	0.3942	0.4141	0.4039	0.9264
0.0022	64.0	2944	0.6010	0.4435	0.2778	0.3416	0.9355
0.0022	65.0	2990	0.6063	0.4244	0.3687	0.3946	0.9319
0.0022	66.0	3036	0.5929	0.4597	0.2879	0.3540	0.9367
0.0022	67.0	3082	0.5940	0.4580	0.3030	0.3647	0.9364
0.0022	68.0	3128	0.6001	0.4690	0.2677	0.3408	0.9377
0.0022	69.0	3174	0.5965	0.4643	0.3283	0.3846	0.9367
0.0022	70.0	3220	0.5986	0.4638	0.3232	0.3810	0.9367
0.0022	71.0	3266	0.6002	0.4610	0.3283	0.3835	0.9364
0.0022	72.0	3312	0.6068	0.4485	0.3081	0.3653	0.9355
0.0022	73.0	3358	0.6082	0.4371	0.3333	0.3782	0.9340
0.0022	74.0	3404	0.6078	0.472	0.2980	0.3653	0.9377
0.0022	75.0	3450	0.6103	0.4570	0.3485	0.3954	0.9358
0.0022	76.0	3496	0.6083	0.4779	0.2727	0.3473	0.9383
0.0021	77.0	3542	0.6045	0.4621	0.3384	0.3907	0.9364
0.0021	78.0	3588	0.6086	0.4643	0.2626	0.3355	0.9373
0.0021	79.0	3634	0.6104	0.4503	0.3434	0.3897	0.9352
0.0021	80.0	3680	0.6063	0.4701	0.3182	0.3795	0.9373
0.0021	81.0	3726	0.6087	0.4641	0.3586	0.4046	0.9364
0.0021	82.0	3772	0.6075	0.4662	0.3485	0.3988	0.9367
0.0021	83.0	3818	0.6074	0.4626	0.3434	0.3942	0.9364
0.0021	84.0	3864	0.6085	0.4636	0.3535	0.4011	0.9364
0.0021	85.0	3910	0.6108	0.4494	0.3586	0.3989	0.9349
0.0021	86.0	3956	0.6080	0.4589	0.3384	0.3895	0.9361
0.002	87.0	4002	0.6090	0.4552	0.3333	0.3848	0.9358
0.002	88.0	4048	0.6099	0.4545	0.3283	0.3812	0.9358
0.002	89.0	4094	0.6080	0.4622	0.2778	0.3470	0.9370
0.002	90.0	4140	0.6097	0.4488	0.2879	0.3508	0.9358
0.002	91.0	4186	0.6107	0.45	0.3182	0.3728	0.9355
0.002	92.0	4232	0.6110	0.4539	0.3232	0.3776	0.9358
0.002	93.0	4278	0.6145	0.4453	0.2879	0.3497	0.9355
0.002	94.0	4324	0.6160	0.4472	0.2778	0.3427	0.9358
0.002	95.0	4370	0.6163	0.448	0.2828	0.3467	0.9358
0.002	96.0	4416	0.6163	0.4412	0.3030	0.3593	0.9349
0.002	97.0	4462	0.6168	0.4388	0.3081	0.3620	0.9346
0.0019	98.0	4508	0.6172	0.4476	0.3232	0.3754	0.9352
0.0019	99.0	4554	0.6175	0.4476	0.3232	0.3754	0.9352
0.0019	100.0	4600	0.6175	0.4476	0.3232	0.3754	0.9352

Framework versions

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

gonzalezrostani/my_awesome_wnut_SAprotection

my_awesome_wnut_SAprotection

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gonzalezrostani/
my_awesome_wnut_SAprotection