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gonzalezrostani/
my_awesome_wnut_Them

Name: my_awesome_wnut_Them
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

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my_awesome_wnut_Them

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: 0.2756
Precision: 0.6327
Recall: 0.6739
F1: 0.6526
Accuracy: 0.9696

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	0.1147	0.3448	0.1087	0.1653	0.9538
No log	2.0	92	0.0827	0.5541	0.4457	0.4940	0.9650
No log	3.0	138	0.0809	0.5521	0.5761	0.5638	0.9684
No log	4.0	184	0.1063	0.5789	0.4783	0.5238	0.9665
No log	5.0	230	0.1163	0.6067	0.5870	0.5967	0.9705
No log	6.0	276	0.1318	0.5955	0.5761	0.5856	0.9699
No log	7.0	322	0.1391	0.5824	0.5761	0.5792	0.9693
No log	8.0	368	0.1472	0.6404	0.6196	0.6298	0.9693
No log	9.0	414	0.1527	0.6264	0.6196	0.6230	0.9699
No log	10.0	460	0.1542	0.5327	0.6196	0.5729	0.9662
0.042	11.0	506	0.1710	0.6211	0.6413	0.6310	0.9690
0.042	12.0	552	0.1607	0.6304	0.6304	0.6304	0.9714
0.042	13.0	598	0.1694	0.6354	0.6630	0.6489	0.9714
0.042	14.0	644	0.1831	0.6703	0.6630	0.6667	0.9711
0.042	15.0	690	0.1737	0.6531	0.6957	0.6737	0.9714
0.042	16.0	736	0.1762	0.6988	0.6304	0.6629	0.9714
0.042	17.0	782	0.1787	0.64	0.6957	0.6667	0.9699
0.042	18.0	828	0.1967	0.6517	0.6304	0.6409	0.9699
0.042	19.0	874	0.2028	0.6742	0.6522	0.6630	0.9693
0.042	20.0	920	0.2211	0.6588	0.6087	0.6328	0.9681
0.042	21.0	966	0.2013	0.6526	0.6739	0.6631	0.9705
0.002	22.0	1012	0.2233	0.6512	0.6087	0.6292	0.9684
0.002	23.0	1058	0.2080	0.6598	0.6957	0.6772	0.9714
0.002	24.0	1104	0.2140	0.6737	0.6957	0.6845	0.9711
0.002	25.0	1150	0.2185	0.6742	0.6522	0.6630	0.9702
0.002	26.0	1196	0.2177	0.6667	0.6522	0.6593	0.9708
0.002	27.0	1242	0.2176	0.6562	0.6848	0.6702	0.9714
0.002	28.0	1288	0.2233	0.6632	0.6848	0.6738	0.9708
0.002	29.0	1334	0.2450	0.6237	0.6304	0.6270	0.9687
0.002	30.0	1380	0.2500	0.6404	0.6196	0.6298	0.9681
0.002	31.0	1426	0.2436	0.6333	0.6196	0.6264	0.9681
0.002	32.0	1472	0.2449	0.6667	0.6087	0.6364	0.9687
0.0006	33.0	1518	0.2324	0.6630	0.6630	0.6630	0.9705
0.0006	34.0	1564	0.2383	0.6548	0.5978	0.6250	0.9681
0.0006	35.0	1610	0.2460	0.6489	0.6630	0.6559	0.9693
0.0006	36.0	1656	0.2445	0.6489	0.6630	0.6559	0.9696
0.0006	37.0	1702	0.2464	0.62	0.6739	0.6458	0.9690
0.0006	38.0	1748	0.2391	0.5714	0.6522	0.6091	0.9659
0.0006	39.0	1794	0.2450	0.6452	0.6522	0.6486	0.9696
0.0006	40.0	1840	0.2434	0.6463	0.5761	0.6092	0.9690
0.0006	41.0	1886	0.2267	0.6632	0.6848	0.6738	0.9720
0.0006	42.0	1932	0.2392	0.6818	0.6522	0.6667	0.9711
0.0006	43.0	1978	0.2377	0.6932	0.6630	0.6778	0.9717
0.0005	44.0	2024	0.2337	0.6562	0.6848	0.6702	0.9708
0.0005	45.0	2070	0.2388	0.6702	0.6848	0.6774	0.9717
0.0005	46.0	2116	0.2298	0.6667	0.6522	0.6593	0.9717
0.0005	47.0	2162	0.2334	0.6742	0.6522	0.6630	0.9717
0.0005	48.0	2208	0.2332	0.6667	0.6522	0.6593	0.9717
0.0005	49.0	2254	0.2350	0.6429	0.6848	0.6632	0.9705
0.0005	50.0	2300	0.2371	0.6495	0.6848	0.6667	0.9708
0.0005	51.0	2346	0.2405	0.6632	0.6848	0.6738	0.9711
0.0005	52.0	2392	0.2468	0.6629	0.6413	0.6519	0.9708
0.0005	53.0	2438	0.2484	0.6452	0.6522	0.6486	0.9705
0.0005	54.0	2484	0.2480	0.6383	0.6522	0.6452	0.9699
0.0001	55.0	2530	0.2495	0.6452	0.6522	0.6486	0.9702
0.0001	56.0	2576	0.2487	0.6495	0.6848	0.6667	0.9711
0.0001	57.0	2622	0.2497	0.6458	0.6739	0.6596	0.9708
0.0001	58.0	2668	0.2505	0.6458	0.6739	0.6596	0.9708
0.0001	59.0	2714	0.2514	0.6495	0.6848	0.6667	0.9714
0.0001	60.0	2760	0.2497	0.6531	0.6957	0.6737	0.9705
0.0001	61.0	2806	0.2584	0.6421	0.6630	0.6524	0.9702
0.0001	62.0	2852	0.2583	0.6263	0.6739	0.6492	0.9702
0.0001	63.0	2898	0.2605	0.6429	0.6848	0.6632	0.9702
0.0001	64.0	2944	0.2617	0.6392	0.6739	0.6561	0.9696
0.0001	65.0	2990	0.2607	0.6429	0.6848	0.6632	0.9705
0.0002	66.0	3036	0.2608	0.6364	0.6848	0.6597	0.9708
0.0002	67.0	3082	0.2650	0.6105	0.6304	0.6203	0.9693
0.0002	68.0	3128	0.2675	0.6277	0.6413	0.6344	0.9693
0.0002	69.0	3174	0.2674	0.6392	0.6739	0.6561	0.9699
0.0002	70.0	3220	0.2713	0.6404	0.6196	0.6298	0.9684
0.0002	71.0	3266	0.2727	0.6392	0.6739	0.6561	0.9690
0.0002	72.0	3312	0.2745	0.6522	0.6522	0.6522	0.9693
0.0002	73.0	3358	0.2707	0.6337	0.6957	0.6632	0.9696
0.0002	74.0	3404	0.2753	0.6344	0.6413	0.6378	0.9684
0.0002	75.0	3450	0.2770	0.6591	0.6304	0.6444	0.9690
0.0002	76.0	3496	0.2776	0.6591	0.6304	0.6444	0.9693
0.0002	77.0	3542	0.2763	0.6591	0.6304	0.6444	0.9690
0.0002	78.0	3588	0.2761	0.6591	0.6304	0.6444	0.9690
0.0002	79.0	3634	0.2770	0.6667	0.6304	0.6480	0.9693
0.0002	80.0	3680	0.2732	0.6421	0.6630	0.6524	0.9696
0.0002	81.0	3726	0.2725	0.6263	0.6739	0.6492	0.9690
0.0002	82.0	3772	0.2736	0.6289	0.6630	0.6455	0.9690
0.0002	83.0	3818	0.2740	0.6289	0.6630	0.6455	0.9690
0.0002	84.0	3864	0.2885	0.6706	0.6196	0.6441	0.9693
0.0002	85.0	3910	0.2869	0.6628	0.6196	0.6404	0.9696
0.0002	86.0	3956	0.2777	0.6484	0.6413	0.6448	0.9687
0.0001	87.0	4002	0.2775	0.6556	0.6413	0.6484	0.9690
0.0001	88.0	4048	0.2773	0.6344	0.6413	0.6378	0.9687
0.0001	89.0	4094	0.2780	0.6413	0.6413	0.6413	0.9690
0.0001	90.0	4140	0.2778	0.6413	0.6413	0.6413	0.9690
0.0001	91.0	4186	0.2777	0.6344	0.6413	0.6378	0.9687
0.0001	92.0	4232	0.2773	0.6383	0.6522	0.6452	0.9693
0.0001	93.0	4278	0.2778	0.6344	0.6413	0.6378	0.9690
0.0001	94.0	4324	0.2778	0.6383	0.6522	0.6452	0.9693
0.0001	95.0	4370	0.2777	0.6383	0.6522	0.6452	0.9693
0.0001	96.0	4416	0.2755	0.6327	0.6739	0.6526	0.9696
0.0001	97.0	4462	0.2756	0.6327	0.6739	0.6526	0.9696
0.0001	98.0	4508	0.2756	0.6327	0.6739	0.6526	0.9696
0.0001	99.0	4554	0.2756	0.6327	0.6739	0.6526	0.9696
0.0001	100.0	4600	0.2756	0.6327	0.6739	0.6526	0.9696

Framework versions

Transformers 4.39.1
Pytorch 2.2.2+cu118
Datasets 2.18.0
Tokenizers 0.15.2

gonzalezrostani/my_awesome_wnut_Them

my_awesome_wnut_Them

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gonzalezrostani/
my_awesome_wnut_Them