distilbert-base-uncased-finetuned-as_sentences

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0627
• Accuracy: 0.9733
• F1: 0.9733

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.6987	1.0	11	0.6958	0.46	0.3025
0.6851	2.0	22	0.6715	0.5667	0.4954
0.6315	3.0	33	0.4515	0.88	0.8791
0.4086	4.0	44	0.1662	0.96	0.9599
0.136	5.0	55	0.0857	0.9667	0.9666
0.0955	6.0	66	0.0661	0.9733	0.9733
0.022	7.0	77	0.0569	0.9667	0.9666
0.0272	8.0	88	0.0626	0.9667	0.9666
0.0346	9.0	99	0.0818	0.9667	0.9666
0.0157	10.0	110	0.0649	0.9667	0.9666
0.0232	11.0	121	0.1416	0.9533	0.9531
0.0202	12.0	132	0.0652	0.9733	0.9733
0.0069	13.0	143	0.0764	0.96	0.9599
0.0032	14.0	154	0.0842	0.9667	0.9666
0.0052	15.0	165	0.0697	0.9667	0.9666
0.0028	16.0	176	0.0773	0.9667	0.9666
0.0066	17.0	187	0.0809	0.9667	0.9667
0.0022	18.0	198	0.0569	0.9667	0.9666
0.002	19.0	209	0.0537	0.9733	0.9733
0.0016	20.0	220	0.0502	0.9733	0.9733
0.0015	21.0	231	0.0460	0.9733	0.9733
0.0013	22.0	242	0.0451	0.9733	0.9733
0.0013	23.0	253	0.0448	0.9733	0.9733
0.0012	24.0	264	0.0450	0.9733	0.9733
0.0012	25.0	275	0.0457	0.9733	0.9733
0.0011	26.0	286	0.0465	0.9733	0.9733
0.0011	27.0	297	0.0466	0.9733	0.9733
0.001	28.0	308	0.0613	0.9667	0.9666
0.001	29.0	319	0.0658	0.9667	0.9666
0.0009	30.0	330	0.0674	0.9667	0.9666
0.0008	31.0	341	0.0693	0.9667	0.9666
0.0009	32.0	352	0.0711	0.9667	0.9666
0.0008	33.0	363	0.0718	0.9667	0.9666
0.0028	34.0	374	0.0824	0.9667	0.9667
0.0011	35.0	385	0.0884	0.9667	0.9666
0.0008	36.0	396	0.1060	0.9667	0.9666
0.0009	37.0	407	0.0875	0.96	0.9599
0.0015	38.0	418	0.0623	0.9667	0.9666
0.0007	39.0	429	0.0610	0.9733	0.9733
0.0007	40.0	440	0.0614	0.9733	0.9733
0.0007	41.0	451	0.0617	0.9733	0.9733
0.0007	42.0	462	0.0618	0.9733	0.9733
0.0006	43.0	473	0.0620	0.9733	0.9733
0.0006	44.0	484	0.0621	0.9733	0.9733
0.0006	45.0	495	0.0622	0.9733	0.9733
0.0006	46.0	506	0.0624	0.9733	0.9733
0.0006	47.0	517	0.0625	0.9733	0.9733
0.0006	48.0	528	0.0626	0.9733	0.9733
0.0006	49.0	539	0.0627	0.9733	0.9733
0.0006	50.0	550	0.0627	0.9733	0.9733

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3