007-microsoft-deberta-v3-base-finetuned-yahoo-80_20k

This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8060
F1: 0.7514
Accuracy: 0.7552
Precision: 0.7512
Recall: 0.7552
System Ram Used: 4.1778
System Ram Total: 83.4807
Gpu Ram Allocated: 2.0903
Gpu Ram Cached: 34.3125
Gpu Ram Total: 39.5640
Gpu Utilization: 44
Disk Space Used: 36.0258
Disk Space Total: 78.1898

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy	Precision	Recall	System Ram Used	System Ram Total	Gpu Ram Allocated	Gpu Ram Cached	Gpu Ram Total	Gpu Utilization	Disk Space Used	Disk Space Total
1.3512	0.15	375	0.9418	0.7160	0.7189	0.7210	0.7189	3.9586	83.4807	2.0903	34.3125	39.5640	42	24.9904	78.1898
0.9581	0.3	750	0.8981	0.7232	0.7298	0.7301	0.7298	3.9108	83.4807	2.0903	34.3125	39.5640	46	24.9906	78.1898
0.9184	0.45	1125	0.8941	0.7248	0.7316	0.7301	0.7316	3.8717	83.4807	2.0903	34.3125	39.5640	46	24.9910	78.1898
0.8716	0.6	1500	0.8481	0.7368	0.7391	0.7414	0.7391	3.9030	83.4807	2.0903	34.3125	39.5640	46	24.9913	78.1898
0.8564	0.75	1875	0.8394	0.7379	0.7440	0.7423	0.7440	3.8964	83.4807	2.0903	34.3125	39.5640	44	24.9915	78.1898
0.8359	0.9	2250	0.8371	0.7347	0.7403	0.7417	0.7403	3.8917	83.4807	2.0903	34.3125	39.5640	48	24.9917	78.1898
0.7896	1.05	2625	0.8277	0.7369	0.7435	0.7461	0.7435	4.1488	83.4807	2.0903	34.3125	39.5640	44	29.8274	78.1898
0.7368	1.2	3000	0.8204	0.7426	0.7473	0.7468	0.7473	4.1447	83.4807	2.0903	34.3125	39.5640	45	29.8276	78.1898
0.72	1.35	3375	0.8199	0.7455	0.7486	0.7467	0.7486	3.9562	83.4807	2.0903	34.3125	39.5640	43	29.8279	78.1898
0.7333	1.5	3750	0.7991	0.7488	0.7524	0.7496	0.7524	3.9475	83.4807	2.0903	34.3125	39.5640	45	29.8282	78.1898
0.7116	1.65	4125	0.8149	0.7470	0.7499	0.7497	0.7499	3.9456	83.4807	2.0903	34.3125	39.5640	43	29.8285	78.1898
0.7177	1.8	4500	0.7880	0.7523	0.7558	0.7529	0.7558	3.9296	83.4807	2.0903	34.3125	39.5640	44	29.8287	78.1898
0.7151	1.95	4875	0.7949	0.7509	0.7540	0.7507	0.7540	3.9427	83.4807	2.0903	34.3125	39.5640	41	29.8294	78.1898
0.657	2.1	5250	0.8097	0.7500	0.7537	0.7506	0.7537	4.1520	83.4807	2.0903	34.3125	39.5640	43	33.9634	78.1898
0.6218	2.25	5625	0.8049	0.7485	0.7528	0.7484	0.7528	4.1390	83.4807	2.0903	34.3125	39.5640	44	33.9635	78.1898
0.6185	2.4	6000	0.8093	0.7511	0.7543	0.7513	0.7543	3.9715	83.4807	2.0903	34.3125	39.5640	42	33.9637	78.1898
0.6271	2.55	6375	0.8019	0.7517	0.7550	0.7521	0.7550	3.9697	83.4807	2.0903	34.3125	39.5640	46	33.9638	78.1898
0.6103	2.7	6750	0.8026	0.7519	0.7554	0.7523	0.7554	3.9622	83.4807	2.0903	34.3125	39.5640	46	33.9639	78.1898
0.6111	2.85	7125	0.8056	0.7507	0.7546	0.7511	0.7546	3.9783	83.4807	2.0903	34.3125	39.5640	41	33.9640	78.1898
0.6015	3.0	7500	0.8060	0.7514	0.7552	0.7512	0.7552	3.9702	83.4807	2.0903	34.3125	39.5640	42	33.9642	78.1898

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

diogopaes10/007-microsoft-deberta-v3-base-finetuned-yahoo-80_20k

007-microsoft-deberta-v3-base-finetuned-yahoo-80_20k

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model