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gonzalezrostani/
my_awesome_wnut_JAOo

Name: my_awesome_wnut_JAOo
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

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my_awesome_wnut_JAOo

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: 0.0780
Precision: 0.6957
Recall: 0.5161
F1: 0.5926
Accuracy: 0.9909

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	0.0806	0.0	0.0	0.0	0.9848
No log	2.0	92	0.0497	0.0	0.0	0.0	0.9848
No log	3.0	138	0.0381	0.8	0.3871	0.5217	0.9900
No log	4.0	184	0.0483	1.0	0.3871	0.5581	0.9897
No log	5.0	230	0.0427	0.875	0.4516	0.5957	0.9912
No log	6.0	276	0.0458	0.7	0.4516	0.5490	0.9897
No log	7.0	322	0.0480	0.6667	0.5161	0.5818	0.9909
No log	8.0	368	0.0430	0.5938	0.6129	0.6032	0.9903
No log	9.0	414	0.0453	0.5455	0.3871	0.4528	0.9891
No log	10.0	460	0.0482	0.6154	0.5161	0.5614	0.9909
0.0206	11.0	506	0.0527	0.7778	0.4516	0.5714	0.9906
0.0206	12.0	552	0.0501	0.6087	0.4516	0.5185	0.9903
0.0206	13.0	598	0.0679	0.9286	0.4194	0.5778	0.9906
0.0206	14.0	644	0.0521	0.6538	0.5484	0.5965	0.9909
0.0206	15.0	690	0.0546	0.6522	0.4839	0.5556	0.9900
0.0206	16.0	736	0.0561	0.7143	0.4839	0.5769	0.9894
0.0206	17.0	782	0.0625	0.8235	0.4516	0.5833	0.9900
0.0206	18.0	828	0.0563	0.6538	0.5484	0.5965	0.9906
0.0206	19.0	874	0.0583	0.6667	0.4516	0.5385	0.9900
0.0206	20.0	920	0.0638	0.6667	0.4516	0.5385	0.9897
0.0206	21.0	966	0.0622	0.6538	0.5484	0.5965	0.9909
0.0008	22.0	1012	0.0634	0.64	0.5161	0.5714	0.9906
0.0008	23.0	1058	0.0627	0.64	0.5161	0.5714	0.9909
0.0008	24.0	1104	0.0636	0.6538	0.5484	0.5965	0.9909
0.0008	25.0	1150	0.0638	0.6667	0.5806	0.6207	0.9912
0.0008	26.0	1196	0.0648	0.6538	0.5484	0.5965	0.9909
0.0008	27.0	1242	0.0672	0.75	0.4839	0.5882	0.9906
0.0008	28.0	1288	0.0659	0.7619	0.5161	0.6154	0.9903
0.0008	29.0	1334	0.0656	0.7727	0.5484	0.6415	0.9906
0.0008	30.0	1380	0.0672	0.75	0.4839	0.5882	0.9900
0.0008	31.0	1426	0.0659	0.6538	0.5484	0.5965	0.9909
0.0008	32.0	1472	0.0667	0.68	0.5484	0.6071	0.9906
0.0005	33.0	1518	0.0675	0.68	0.5484	0.6071	0.9909
0.0005	34.0	1564	0.0681	0.6818	0.4839	0.5660	0.9900
0.0005	35.0	1610	0.0687	0.6667	0.5161	0.5818	0.9906
0.0005	36.0	1656	0.0700	0.75	0.4839	0.5882	0.9900
0.0005	37.0	1702	0.0708	0.75	0.4839	0.5882	0.9903
0.0005	38.0	1748	0.0713	0.75	0.4839	0.5882	0.9903
0.0005	39.0	1794	0.0717	0.75	0.4839	0.5882	0.9903
0.0005	40.0	1840	0.0705	0.6667	0.5161	0.5818	0.9903
0.0005	41.0	1886	0.0710	0.6522	0.4839	0.5556	0.9903
0.0005	42.0	1932	0.0727	0.75	0.4839	0.5882	0.9903
0.0005	43.0	1978	0.0718	0.6522	0.4839	0.5556	0.9903
0.0004	44.0	2024	0.0724	0.6522	0.4839	0.5556	0.9903
0.0004	45.0	2070	0.0723	0.6522	0.4839	0.5556	0.9900
0.0004	46.0	2116	0.0732	0.75	0.4839	0.5882	0.9900
0.0004	47.0	2162	0.0734	0.7143	0.4839	0.5769	0.9903
0.0004	48.0	2208	0.0734	0.7143	0.4839	0.5769	0.9903
0.0004	49.0	2254	0.0734	0.7143	0.4839	0.5769	0.9903
0.0004	50.0	2300	0.0736	0.7143	0.4839	0.5769	0.9906
0.0004	51.0	2346	0.0740	0.75	0.4839	0.5882	0.9903
0.0004	52.0	2392	0.0736	0.7143	0.4839	0.5769	0.9903
0.0004	53.0	2438	0.0747	0.75	0.4839	0.5882	0.9903
0.0004	54.0	2484	0.0746	0.75	0.4839	0.5882	0.9900
0.0003	55.0	2530	0.0753	0.75	0.4839	0.5882	0.9903
0.0003	56.0	2576	0.0744	0.7143	0.4839	0.5769	0.9903
0.0003	57.0	2622	0.0742	0.6667	0.5161	0.5818	0.9906
0.0003	58.0	2668	0.0747	0.6667	0.5161	0.5818	0.9906
0.0003	59.0	2714	0.0754	0.7143	0.4839	0.5769	0.9903
0.0003	60.0	2760	0.0754	0.7143	0.4839	0.5769	0.9903
0.0003	61.0	2806	0.0752	0.6667	0.5161	0.5818	0.9906
0.0003	62.0	2852	0.0757	0.6818	0.4839	0.5660	0.9900
0.0003	63.0	2898	0.0756	0.68	0.5484	0.6071	0.9909
0.0003	64.0	2944	0.0756	0.68	0.5484	0.6071	0.9906
0.0003	65.0	2990	0.0761	0.68	0.5484	0.6071	0.9909
0.0003	66.0	3036	0.0755	0.68	0.5484	0.6071	0.9906
0.0003	67.0	3082	0.0760	0.68	0.5484	0.6071	0.9909
0.0003	68.0	3128	0.0762	0.6957	0.5161	0.5926	0.9903
0.0003	69.0	3174	0.0764	0.6957	0.5161	0.5926	0.9903
0.0003	70.0	3220	0.0765	0.6957	0.5161	0.5926	0.9903
0.0003	71.0	3266	0.0767	0.75	0.4839	0.5882	0.9900
0.0003	72.0	3312	0.0765	0.6957	0.5161	0.5926	0.9903
0.0003	73.0	3358	0.0763	0.7143	0.4839	0.5769	0.9903
0.0003	74.0	3404	0.0765	0.7273	0.5161	0.6038	0.9906
0.0003	75.0	3450	0.0766	0.7273	0.5161	0.6038	0.9906
0.0003	76.0	3496	0.0767	0.7273	0.5161	0.6038	0.9912
0.0004	77.0	3542	0.0763	0.7273	0.5161	0.6038	0.9906
0.0004	78.0	3588	0.0765	0.7273	0.5161	0.6038	0.9906
0.0004	79.0	3634	0.0771	0.75	0.4839	0.5882	0.9906
0.0004	80.0	3680	0.0769	0.7273	0.5161	0.6038	0.9912
0.0004	81.0	3726	0.0771	0.7273	0.5161	0.6038	0.9906
0.0004	82.0	3772	0.0770	0.7273	0.5161	0.6038	0.9912
0.0004	83.0	3818	0.0772	0.7273	0.5161	0.6038	0.9912
0.0004	84.0	3864	0.0775	0.7273	0.5161	0.6038	0.9912
0.0004	85.0	3910	0.0775	0.7273	0.5161	0.6038	0.9912
0.0004	86.0	3956	0.0775	0.7273	0.5161	0.6038	0.9909
0.0003	87.0	4002	0.0774	0.7273	0.5161	0.6038	0.9906
0.0003	88.0	4048	0.0776	0.6957	0.5161	0.5926	0.9909
0.0003	89.0	4094	0.0776	0.7083	0.5484	0.6182	0.9912
0.0003	90.0	4140	0.0777	0.68	0.5484	0.6071	0.9909
0.0003	91.0	4186	0.0777	0.68	0.5484	0.6071	0.9909
0.0003	92.0	4232	0.0776	0.68	0.5484	0.6071	0.9909
0.0003	93.0	4278	0.0778	0.6957	0.5161	0.5926	0.9909
0.0003	94.0	4324	0.0779	0.6957	0.5161	0.5926	0.9909
0.0003	95.0	4370	0.0779	0.6957	0.5161	0.5926	0.9909
0.0003	96.0	4416	0.0780	0.6957	0.5161	0.5926	0.9909
0.0003	97.0	4462	0.0779	0.6957	0.5161	0.5926	0.9909
0.0003	98.0	4508	0.0780	0.6957	0.5161	0.5926	0.9909
0.0003	99.0	4554	0.0780	0.6957	0.5161	0.5926	0.9909
0.0003	100.0	4600	0.0780	0.6957	0.5161	0.5926	0.9909

Framework versions

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

gonzalezrostani/my_awesome_wnut_JAOo

my_awesome_wnut_JAOo

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gonzalezrostani/
my_awesome_wnut_JAOo