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gonzalezrostani/
my_awesome_wnut_JHn

Name: my_awesome_wnut_JHn
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

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my_awesome_wnut_JHn

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: 0.3507
Precision: 0.5933
Recall: 0.6794
F1: 0.6335
Accuracy: 0.9605

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	0.1388	0.7083	0.2595	0.3799	0.9489
No log	2.0	92	0.1106	0.6087	0.6412	0.6245	0.9629
No log	3.0	138	0.1076	0.5687	0.6947	0.6254	0.9589
No log	4.0	184	0.1143	0.5987	0.7176	0.6528	0.9614
No log	5.0	230	0.1348	0.5583	0.6947	0.6190	0.9568
No log	6.0	276	0.1534	0.5897	0.7023	0.6411	0.9599
No log	7.0	322	0.1800	0.6014	0.6565	0.6277	0.9586
No log	8.0	368	0.1854	0.5921	0.6870	0.6360	0.9595
No log	9.0	414	0.2003	0.6304	0.6641	0.6468	0.9602
No log	10.0	460	0.2015	0.6385	0.6336	0.6360	0.9617
0.0611	11.0	506	0.2170	0.5909	0.6947	0.6386	0.9592
0.0611	12.0	552	0.2503	0.6183	0.6183	0.6183	0.9602
0.0611	13.0	598	0.2504	0.6136	0.6183	0.6160	0.9605
0.0611	14.0	644	0.2367	0.5963	0.7328	0.6575	0.9605
0.0611	15.0	690	0.2670	0.6087	0.6412	0.6245	0.9599
0.0611	16.0	736	0.2508	0.6164	0.6870	0.6498	0.9608
0.0611	17.0	782	0.2633	0.6174	0.7023	0.6571	0.9614
0.0611	18.0	828	0.2668	0.6026	0.6947	0.6454	0.9602
0.0611	19.0	874	0.2646	0.6013	0.7023	0.6479	0.9608
0.0611	20.0	920	0.2681	0.6429	0.6870	0.6642	0.9629
0.0611	21.0	966	0.2831	0.6286	0.6718	0.6494	0.9623
0.0025	22.0	1012	0.2565	0.5706	0.7099	0.6327	0.9605
0.0025	23.0	1058	0.2579	0.6090	0.7252	0.6620	0.9617
0.0025	24.0	1104	0.2756	0.6301	0.7023	0.6643	0.9617
0.0025	25.0	1150	0.2927	0.6233	0.6947	0.6570	0.9623
0.0025	26.0	1196	0.2896	0.6026	0.6947	0.6454	0.9608
0.0025	27.0	1242	0.2652	0.5860	0.7023	0.6389	0.9614
0.0025	28.0	1288	0.2657	0.5680	0.7328	0.6400	0.9617
0.0025	29.0	1334	0.2659	0.6013	0.7023	0.6479	0.9623
0.0025	30.0	1380	0.2808	0.6241	0.6718	0.6471	0.9635
0.0025	31.0	1426	0.2847	0.6383	0.6870	0.6618	0.9641
0.0025	32.0	1472	0.2855	0.6078	0.7099	0.6549	0.9638
0.0012	33.0	1518	0.2897	0.6118	0.7099	0.6572	0.9638
0.0012	34.0	1564	0.2854	0.6234	0.7328	0.6737	0.9635
0.0012	35.0	1610	0.2869	0.6424	0.7405	0.6879	0.9653
0.0012	36.0	1656	0.2943	0.6291	0.7252	0.6738	0.9650
0.0012	37.0	1702	0.3027	0.6284	0.7099	0.6667	0.9641
0.0012	38.0	1748	0.3029	0.6242	0.7099	0.6643	0.9641
0.0012	39.0	1794	0.3038	0.6333	0.7252	0.6762	0.9650
0.0012	40.0	1840	0.2969	0.6013	0.7252	0.6574	0.9638
0.0012	41.0	1886	0.3025	0.6259	0.7023	0.6619	0.9635
0.0012	42.0	1932	0.2950	0.6194	0.7328	0.6713	0.9641
0.0012	43.0	1978	0.3180	0.6458	0.7099	0.6764	0.9638
0.0007	44.0	2024	0.3047	0.6178	0.7405	0.6736	0.9635
0.0007	45.0	2070	0.3149	0.6284	0.7099	0.6667	0.9632
0.0007	46.0	2116	0.3183	0.6370	0.6565	0.6466	0.9635
0.0007	47.0	2162	0.3040	0.5962	0.7099	0.6481	0.9632
0.0007	48.0	2208	0.3037	0.5962	0.7099	0.6481	0.9635
0.0007	49.0	2254	0.3091	0.6333	0.7252	0.6762	0.9641
0.0007	50.0	2300	0.3157	0.6284	0.7099	0.6667	0.9626
0.0007	51.0	2346	0.3144	0.6174	0.7023	0.6571	0.9626
0.0007	52.0	2392	0.3298	0.6418	0.6565	0.6491	0.9644
0.0007	53.0	2438	0.3106	0.6065	0.7176	0.6573	0.9632
0.0007	54.0	2484	0.3075	0.6345	0.7023	0.6667	0.9641
0.0008	55.0	2530	0.3191	0.6370	0.6565	0.6466	0.9629
0.0008	56.0	2576	0.3270	0.6515	0.6565	0.6540	0.9638
0.0008	57.0	2622	0.3199	0.6414	0.7099	0.6739	0.9629
0.0008	58.0	2668	0.3315	0.6515	0.6565	0.6540	0.9638
0.0008	59.0	2714	0.3208	0.6414	0.7099	0.6739	0.9638
0.0008	60.0	2760	0.3246	0.6434	0.7023	0.6715	0.9635
0.0008	61.0	2806	0.3301	0.6301	0.7023	0.6643	0.9629
0.0008	62.0	2852	0.3145	0.5935	0.7023	0.6434	0.9623
0.0008	63.0	2898	0.3167	0.6319	0.6947	0.6618	0.9632
0.0008	64.0	2944	0.3191	0.6149	0.6947	0.6523	0.9626
0.0008	65.0	2990	0.3262	0.6268	0.6794	0.6520	0.9626
0.0006	66.0	3036	0.3209	0.6013	0.7023	0.6479	0.9629
0.0006	67.0	3082	0.3457	0.6439	0.6489	0.6464	0.9623
0.0006	68.0	3128	0.3435	0.6331	0.6718	0.6519	0.9620
0.0006	69.0	3174	0.3482	0.6304	0.6641	0.6468	0.9611
0.0006	70.0	3220	0.3423	0.6054	0.6794	0.6403	0.9611
0.0006	71.0	3266	0.3459	0.6222	0.6412	0.6316	0.9611
0.0006	72.0	3312	0.3391	0.5933	0.6794	0.6335	0.9611
0.0006	73.0	3358	0.3391	0.6040	0.6870	0.6429	0.9617
0.0006	74.0	3404	0.3302	0.5915	0.7405	0.6576	0.9605
0.0006	75.0	3450	0.3293	0.6026	0.7176	0.6551	0.9608
0.0006	76.0	3496	0.3332	0.5987	0.6947	0.6431	0.9605
0.0006	77.0	3542	0.3343	0.6067	0.6947	0.6477	0.9611
0.0006	78.0	3588	0.3390	0.6122	0.6870	0.6475	0.9614
0.0006	79.0	3634	0.3393	0.6014	0.6794	0.6380	0.9614
0.0006	80.0	3680	0.3336	0.5844	0.6870	0.6316	0.9602
0.0006	81.0	3726	0.3439	0.5959	0.6641	0.6282	0.9608
0.0006	82.0	3772	0.3433	0.6014	0.6794	0.6380	0.9614
0.0006	83.0	3818	0.3456	0.6084	0.6641	0.6350	0.9623
0.0006	84.0	3864	0.3486	0.5878	0.6641	0.6237	0.9605
0.0006	85.0	3910	0.3480	0.5839	0.6641	0.6214	0.9599
0.0006	86.0	3956	0.3514	0.6084	0.6641	0.6350	0.9611
0.0005	87.0	4002	0.3441	0.5894	0.6794	0.6312	0.9602
0.0005	88.0	4048	0.3483	0.5986	0.6718	0.6331	0.9605
0.0005	89.0	4094	0.3478	0.5946	0.6718	0.6308	0.9602
0.0005	90.0	4140	0.3493	0.5986	0.6718	0.6331	0.9605
0.0005	91.0	4186	0.3513	0.5850	0.6565	0.6187	0.9599
0.0005	92.0	4232	0.3532	0.5972	0.6565	0.6255	0.9605
0.0005	93.0	4278	0.3564	0.6071	0.6489	0.6273	0.9611
0.0005	94.0	4324	0.3561	0.6028	0.6489	0.625	0.9608
0.0005	95.0	4370	0.3523	0.5850	0.6565	0.6187	0.9602
0.0005	96.0	4416	0.3473	0.5894	0.6794	0.6312	0.9602
0.0005	97.0	4462	0.3499	0.5933	0.6794	0.6335	0.9605
0.0004	98.0	4508	0.3503	0.5933	0.6794	0.6335	0.9605
0.0004	99.0	4554	0.3507	0.5933	0.6794	0.6335	0.9605
0.0004	100.0	4600	0.3507	0.5933	0.6794	0.6335	0.9605

Framework versions

Transformers 4.39.1
Pytorch 2.2.2+cu118
Datasets 2.18.0
Tokenizers 0.15.2

gonzalezrostani/my_awesome_wnut_JHn

my_awesome_wnut_JHn

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gonzalezrostani/
my_awesome_wnut_JHn