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gonzalezrostani/
my_awesome_wnut_GAneither

Name: my_awesome_wnut_GAneither
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

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my_awesome_wnut_GAneither

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: 0.4347
Precision: 0.6390
Recall: 0.6725
F1: 0.6553
Accuracy: 0.9492

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	0.1668	0.4885	0.5546	0.5194	0.9340
No log	2.0	92	0.1436	0.6	0.6288	0.6141	0.9465
No log	3.0	138	0.1509	0.5980	0.5197	0.5561	0.9446
No log	4.0	184	0.1417	0.6109	0.6376	0.6239	0.9489
No log	5.0	230	0.1714	0.6123	0.6070	0.6096	0.9450
No log	6.0	276	0.1965	0.6138	0.6594	0.6358	0.9483
No log	7.0	322	0.2061	0.6157	0.6856	0.6488	0.9486
No log	8.0	368	0.2316	0.6387	0.6638	0.6510	0.9495
No log	9.0	414	0.2577	0.6118	0.6332	0.6223	0.9468
No log	10.0	460	0.2604	0.6137	0.6245	0.6190	0.9453
0.0757	11.0	506	0.3047	0.6580	0.5546	0.6019	0.9474
0.0757	12.0	552	0.2688	0.5962	0.6769	0.6339	0.9462
0.0757	13.0	598	0.2777	0.6371	0.6594	0.6481	0.9498
0.0757	14.0	644	0.2916	0.6697	0.6376	0.6532	0.9513
0.0757	15.0	690	0.3116	0.6635	0.6114	0.6364	0.9507
0.0757	16.0	736	0.2955	0.6509	0.6594	0.6551	0.9495
0.0757	17.0	782	0.3101	0.6481	0.6594	0.6537	0.9507
0.0757	18.0	828	0.3259	0.6296	0.6681	0.6483	0.9483
0.0757	19.0	874	0.3509	0.6411	0.5852	0.6119	0.9474
0.0757	20.0	920	0.3201	0.6129	0.6638	0.6373	0.9471
0.0757	21.0	966	0.3251	0.6282	0.6419	0.6350	0.9483
0.0062	22.0	1012	0.3414	0.6432	0.6376	0.6404	0.9489
0.0062	23.0	1058	0.3261	0.6187	0.6943	0.6543	0.9489
0.0062	24.0	1104	0.3364	0.6115	0.6943	0.6503	0.9480
0.0062	25.0	1150	0.3460	0.6387	0.6638	0.6510	0.9513
0.0062	26.0	1196	0.3616	0.6327	0.6769	0.6540	0.9498
0.0062	27.0	1242	0.3651	0.6277	0.6332	0.6304	0.9498
0.0062	28.0	1288	0.3759	0.6452	0.6114	0.6278	0.9480
0.0062	29.0	1334	0.3972	0.6140	0.5764	0.5946	0.9468
0.0062	30.0	1380	0.3798	0.6337	0.6725	0.6525	0.9483
0.0062	31.0	1426	0.3731	0.6468	0.6638	0.6552	0.9501
0.0062	32.0	1472	0.3828	0.6458	0.6769	0.6610	0.9510
0.002	33.0	1518	0.3907	0.6202	0.6987	0.6571	0.9477
0.002	34.0	1564	0.3871	0.6352	0.6769	0.6554	0.9483
0.002	35.0	1610	0.3859	0.6220	0.6681	0.6442	0.9468
0.002	36.0	1656	0.4158	0.6385	0.5939	0.6154	0.9477
0.002	37.0	1702	0.4385	0.6244	0.5590	0.5899	0.9456
0.002	38.0	1748	0.3967	0.6303	0.6550	0.6424	0.9486
0.002	39.0	1794	0.4052	0.6481	0.6594	0.6537	0.9507
0.002	40.0	1840	0.4050	0.6533	0.6419	0.6476	0.9501
0.002	41.0	1886	0.4096	0.6376	0.6376	0.6376	0.9486
0.002	42.0	1932	0.3997	0.6364	0.7031	0.6680	0.9492
0.002	43.0	1978	0.3992	0.6157	0.6856	0.6488	0.9465
0.0013	44.0	2024	0.3896	0.625	0.6769	0.6499	0.9483
0.0013	45.0	2070	0.3938	0.6300	0.6245	0.6272	0.9489
0.0013	46.0	2116	0.4150	0.6406	0.6070	0.6233	0.9495
0.0013	47.0	2162	0.3988	0.6387	0.6638	0.6510	0.9504
0.0013	48.0	2208	0.3993	0.6375	0.6681	0.6525	0.9501
0.0013	49.0	2254	0.4058	0.6138	0.6594	0.6358	0.9477
0.0013	50.0	2300	0.4048	0.6322	0.6681	0.6497	0.9498
0.0013	51.0	2346	0.4029	0.6318	0.6594	0.6453	0.9492
0.0013	52.0	2392	0.4081	0.6398	0.6594	0.6495	0.9501
0.0013	53.0	2438	0.4143	0.6383	0.6550	0.6466	0.9495
0.0013	54.0	2484	0.4056	0.6136	0.7074	0.6572	0.9468
0.0012	55.0	2530	0.4059	0.6382	0.6856	0.6611	0.9489
0.0012	56.0	2576	0.4117	0.6393	0.6812	0.6596	0.9498
0.0012	57.0	2622	0.4238	0.6292	0.6594	0.6439	0.9489
0.0012	58.0	2668	0.4202	0.6220	0.6681	0.6442	0.9483
0.0012	59.0	2714	0.4178	0.6466	0.6550	0.6508	0.9507
0.0012	60.0	2760	0.4098	0.6245	0.6900	0.6556	0.9483
0.0012	61.0	2806	0.4107	0.6537	0.6594	0.6565	0.9513
0.0012	62.0	2852	0.4085	0.6498	0.6725	0.6609	0.9516
0.0012	63.0	2898	0.4116	0.6337	0.6725	0.6525	0.9501
0.0012	64.0	2944	0.4124	0.6305	0.6856	0.6569	0.9492
0.0012	65.0	2990	0.4166	0.6533	0.6419	0.6476	0.9516
0.0009	66.0	3036	0.4081	0.6270	0.6681	0.6469	0.9507
0.0009	67.0	3082	0.4050	0.6417	0.6725	0.6567	0.9510
0.0009	68.0	3128	0.4057	0.6488	0.6856	0.6667	0.9523
0.0009	69.0	3174	0.4080	0.6583	0.6900	0.6738	0.9535
0.0009	70.0	3220	0.4114	0.6569	0.6856	0.6709	0.9532
0.0009	71.0	3266	0.4232	0.6579	0.6550	0.6565	0.9519
0.0009	72.0	3312	0.4120	0.6466	0.7031	0.6736	0.9516
0.0009	73.0	3358	0.4259	0.6594	0.6594	0.6594	0.9519
0.0009	74.0	3404	0.4172	0.6475	0.6900	0.6681	0.9513
0.0009	75.0	3450	0.4175	0.6434	0.6856	0.6638	0.9510
0.0009	76.0	3496	0.4255	0.6522	0.6550	0.6536	0.9510
0.0008	77.0	3542	0.4255	0.6420	0.6812	0.6610	0.9498
0.0008	78.0	3588	0.4245	0.6429	0.6681	0.6552	0.9495
0.0008	79.0	3634	0.4173	0.6449	0.6900	0.6667	0.9507
0.0008	80.0	3680	0.4227	0.6494	0.6550	0.6522	0.9510
0.0008	81.0	3726	0.4210	0.6527	0.6812	0.6667	0.9513
0.0008	82.0	3772	0.4234	0.6437	0.6943	0.6681	0.9504
0.0008	83.0	3818	0.4341	0.6483	0.6681	0.6581	0.9507
0.0008	84.0	3864	0.4355	0.6483	0.6681	0.6581	0.9507
0.0008	85.0	3910	0.4309	0.6393	0.6812	0.6596	0.9501
0.0008	86.0	3956	0.4365	0.6524	0.6638	0.6580	0.9513
0.0007	87.0	4002	0.4394	0.6594	0.6594	0.6594	0.9516
0.0007	88.0	4048	0.4357	0.6552	0.6638	0.6594	0.9513
0.0007	89.0	4094	0.4337	0.6471	0.6725	0.6595	0.9507
0.0007	90.0	4140	0.4350	0.6471	0.6725	0.6595	0.9507
0.0007	91.0	4186	0.4372	0.6525	0.6725	0.6624	0.9516
0.0007	92.0	4232	0.4340	0.6337	0.6725	0.6525	0.9489
0.0007	93.0	4278	0.4330	0.6341	0.6812	0.6568	0.9495
0.0007	94.0	4324	0.4348	0.6417	0.6725	0.6567	0.9498
0.0007	95.0	4370	0.4347	0.6402	0.6681	0.6538	0.9495
0.0007	96.0	4416	0.4351	0.6402	0.6681	0.6538	0.9495
0.0007	97.0	4462	0.4347	0.6390	0.6725	0.6553	0.9492
0.0006	98.0	4508	0.4346	0.6390	0.6725	0.6553	0.9495
0.0006	99.0	4554	0.4348	0.6390	0.6725	0.6553	0.9492
0.0006	100.0	4600	0.4347	0.6390	0.6725	0.6553	0.9492

Framework versions

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

gonzalezrostani/my_awesome_wnut_GAneither

my_awesome_wnut_GAneither

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gonzalezrostani/
my_awesome_wnut_GAneither