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gonzalezrostani/
my_awesome_wnut_SAviolation

Name: my_awesome_wnut_SAviolation
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

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my_awesome_wnut_SAviolation

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: 0.7560
Precision: 0.6859
Recall: 0.3680
F1: 0.4790
Accuracy: 0.9133

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	0.2511	1.0	0.0028	0.0056	0.8920
No log	2.0	92	0.2236	0.7174	0.2781	0.4008	0.9100
No log	3.0	138	0.2291	0.7157	0.4101	0.5214	0.9185
No log	4.0	184	0.2611	0.7010	0.4017	0.5107	0.9167
No log	5.0	230	0.2917	0.6473	0.4073	0.5	0.9118
No log	6.0	276	0.3315	0.6941	0.4270	0.5287	0.9176
No log	7.0	322	0.3815	0.7216	0.3567	0.4774	0.9155
No log	8.0	368	0.3705	0.6768	0.3764	0.4838	0.9130
No log	9.0	414	0.3959	0.6054	0.4438	0.5122	0.9085
No log	10.0	460	0.4755	0.7056	0.3567	0.4739	0.9142
0.083	11.0	506	0.4632	0.6731	0.3933	0.4965	0.9136
0.083	12.0	552	0.4637	0.6683	0.3904	0.4929	0.9130
0.083	13.0	598	0.5068	0.7299	0.3567	0.4792	0.9161
0.083	14.0	644	0.5313	0.6973	0.3624	0.4769	0.9139
0.083	15.0	690	0.5217	0.6919	0.3848	0.4946	0.9148
0.083	16.0	736	0.5284	0.6746	0.3961	0.4991	0.9139
0.083	17.0	782	0.5759	0.75	0.3624	0.4886	0.9179
0.083	18.0	828	0.5507	0.7021	0.3708	0.4853	0.9148
0.083	19.0	874	0.5389	0.7041	0.3876	0.5	0.9161
0.083	20.0	920	0.5858	0.7182	0.3652	0.4842	0.9158
0.083	21.0	966	0.6216	0.7305	0.3427	0.4665	0.9151
0.0039	22.0	1012	0.6282	0.6839	0.3708	0.4809	0.9133
0.0039	23.0	1058	0.6184	0.6512	0.3933	0.4904	0.9115
0.0039	24.0	1104	0.6594	0.7438	0.3343	0.4612	0.9155
0.0039	25.0	1150	0.6154	0.6761	0.4045	0.5062	0.9145
0.0039	26.0	1196	0.5952	0.6478	0.4185	0.5085	0.9124
0.0039	27.0	1242	0.6547	0.7143	0.3511	0.4708	0.9145
0.0039	28.0	1288	0.6535	0.7232	0.3596	0.4803	0.9158
0.0039	29.0	1334	0.6911	0.7093	0.3427	0.4621	0.9136
0.0039	30.0	1380	0.6582	0.6514	0.3989	0.4948	0.9118
0.0039	31.0	1426	0.6655	0.6809	0.3596	0.4706	0.9124
0.0039	32.0	1472	0.6449	0.7330	0.3624	0.4850	0.9167
0.0016	33.0	1518	0.6470	0.7016	0.3764	0.4899	0.9151
0.0016	34.0	1564	0.6737	0.7368	0.3539	0.4782	0.9164
0.0016	35.0	1610	0.6587	0.7010	0.3820	0.4945	0.9155
0.0016	36.0	1656	0.6679	0.7158	0.3820	0.4982	0.9167
0.0016	37.0	1702	0.6760	0.6959	0.3792	0.4909	0.9148
0.0016	38.0	1748	0.6732	0.6927	0.3736	0.4854	0.9142
0.0016	39.0	1794	0.6913	0.6935	0.3624	0.4760	0.9136
0.0016	40.0	1840	0.6781	0.6869	0.3820	0.4910	0.9142
0.0016	41.0	1886	0.6606	0.6939	0.3820	0.4928	0.9148
0.0016	42.0	1932	0.6978	0.6786	0.3736	0.4819	0.9130
0.0016	43.0	1978	0.7024	0.6771	0.3652	0.4745	0.9124
0.001	44.0	2024	0.6970	0.6798	0.3876	0.4937	0.9139
0.001	45.0	2070	0.7026	0.6995	0.3792	0.4918	0.9151
0.001	46.0	2116	0.7356	0.7707	0.3399	0.4717	0.9176
0.001	47.0	2162	0.7028	0.6816	0.3848	0.4919	0.9139
0.001	48.0	2208	0.7466	0.7986	0.3230	0.4600	0.9179
0.001	49.0	2254	0.7013	0.6816	0.3848	0.4919	0.9139
0.001	50.0	2300	0.7368	0.7396	0.3511	0.4762	0.9164
0.001	51.0	2346	0.6903	0.6571	0.3876	0.4876	0.9118
0.001	52.0	2392	0.7091	0.6821	0.3736	0.4828	0.9133
0.001	53.0	2438	0.7316	0.7771	0.3427	0.4756	0.9182
0.001	54.0	2484	0.7388	0.7658	0.3399	0.4708	0.9173
0.0011	55.0	2530	0.6850	0.6498	0.3961	0.4921	0.9115
0.0011	56.0	2576	0.7155	0.7384	0.3567	0.4811	0.9167
0.0011	57.0	2622	0.7198	0.7412	0.3539	0.4791	0.9167
0.0011	58.0	2668	0.7185	0.7384	0.3567	0.4811	0.9167
0.0011	59.0	2714	0.7268	0.7368	0.3539	0.4782	0.9164
0.0011	60.0	2760	0.7304	0.7368	0.3539	0.4782	0.9164
0.0011	61.0	2806	0.6969	0.6980	0.3961	0.5054	0.9161
0.0011	62.0	2852	0.7150	0.7112	0.3736	0.4899	0.9158
0.0011	63.0	2898	0.7355	0.7356	0.3596	0.4830	0.9167
0.0011	64.0	2944	0.7353	0.7273	0.3596	0.4812	0.9161
0.0011	65.0	2990	0.7316	0.7278	0.3680	0.4888	0.9167
0.0006	66.0	3036	0.7547	0.7427	0.3567	0.4820	0.9170
0.0006	67.0	3082	0.7511	0.7429	0.3652	0.4896	0.9176
0.0006	68.0	3128	0.7157	0.6912	0.3961	0.5036	0.9155
0.0006	69.0	3174	0.7336	0.7228	0.3736	0.4926	0.9167
0.0006	70.0	3220	0.7437	0.7314	0.3596	0.4821	0.9164
0.0006	71.0	3266	0.7456	0.7330	0.3624	0.4850	0.9167
0.0006	72.0	3312	0.7234	0.7098	0.3848	0.4991	0.9164
0.0006	73.0	3358	0.7170	0.6779	0.3961	0.5	0.9142
0.0006	74.0	3404	0.7316	0.6927	0.3736	0.4854	0.9142
0.0006	75.0	3450	0.7394	0.7021	0.3708	0.4853	0.9148
0.0006	76.0	3496	0.7417	0.7059	0.3708	0.4862	0.9151
0.0005	77.0	3542	0.7452	0.6856	0.3736	0.4836	0.9136
0.0005	78.0	3588	0.7536	0.7005	0.3680	0.4825	0.9145
0.0005	79.0	3634	0.7334	0.6577	0.4101	0.5052	0.9130
0.0005	80.0	3680	0.7485	0.6859	0.3680	0.4790	0.9133
0.0005	81.0	3726	0.7356	0.6746	0.3961	0.4991	0.9139
0.0005	82.0	3772	0.7436	0.6853	0.3792	0.4882	0.9139
0.0005	83.0	3818	0.7463	0.6804	0.3708	0.48	0.9130
0.0005	84.0	3864	0.7487	0.6804	0.3708	0.48	0.9130
0.0005	85.0	3910	0.7494	0.6804	0.3708	0.48	0.9130
0.0005	86.0	3956	0.7515	0.6804	0.3708	0.48	0.9130
0.0005	87.0	4002	0.7566	0.6823	0.3680	0.4781	0.9130
0.0005	88.0	4048	0.7594	0.6915	0.3652	0.4779	0.9136
0.0005	89.0	4094	0.7644	0.6989	0.3652	0.4797	0.9142
0.0005	90.0	4140	0.7566	0.6931	0.3680	0.4807	0.9139
0.0005	91.0	4186	0.7575	0.6968	0.3680	0.4816	0.9142
0.0005	92.0	4232	0.7600	0.6952	0.3652	0.4788	0.9139
0.0005	93.0	4278	0.7601	0.6915	0.3652	0.4779	0.9136
0.0005	94.0	4324	0.7596	0.6931	0.3680	0.4807	0.9139
0.0005	95.0	4370	0.7603	0.6915	0.3652	0.4779	0.9136
0.0005	96.0	4416	0.7606	0.6915	0.3652	0.4779	0.9136
0.0005	97.0	4462	0.7609	0.6915	0.3652	0.4779	0.9136
0.0004	98.0	4508	0.7575	0.6859	0.3680	0.4790	0.9133
0.0004	99.0	4554	0.7560	0.6859	0.3680	0.4790	0.9133
0.0004	100.0	4600	0.7560	0.6859	0.3680	0.4790	0.9133

Framework versions

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

gonzalezrostani/my_awesome_wnut_SAviolation

my_awesome_wnut_SAviolation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gonzalezrostani/
my_awesome_wnut_SAviolation