distilbert-base-uncased-finetuned-as_sentences_fewshot

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: 0.0227
• Accuracy: 0.9933
• F1: 0.9933

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
0.6953	1.0	11	0.6832	0.6267	0.5993
0.6562	2.0	22	0.5071	0.9267	0.9268
0.4346	3.0	33	0.1365	0.9933	0.9933
0.1714	4.0	44	0.0566	0.9933	0.9933
0.1125	5.0	55	0.0234	1.0	1.0
0.0897	6.0	66	0.0264	0.9933	0.9933
0.0487	7.0	77	0.0465	0.9867	0.9867
0.0401	8.0	88	0.0082	1.0	1.0
0.0364	9.0	99	0.0273	0.9933	0.9933
0.0237	10.0	110	0.0163	0.9933	0.9933
0.0209	11.0	121	0.0044	1.0	1.0
0.0196	12.0	132	0.0056	1.0	1.0
0.0198	13.0	143	0.0059	1.0	1.0
0.0047	14.0	154	0.0063	1.0	1.0
0.0157	15.0	165	0.0115	0.9933	0.9933
0.0142	16.0	176	0.0116	0.9933	0.9933
0.0035	17.0	187	0.0111	0.9933	0.9933
0.0028	18.0	198	0.0114	0.9933	0.9933
0.0023	19.0	209	0.0103	0.9933	0.9933
0.0019	20.0	220	0.0102	0.9933	0.9933
0.0016	21.0	231	0.0117	0.9933	0.9933
0.0016	22.0	242	0.0103	0.9933	0.9933
0.0014	23.0	253	0.0072	0.9933	0.9933
0.0014	24.0	264	0.0059	0.9933	0.9933
0.0013	25.0	275	0.0071	0.9933	0.9933
0.0012	26.0	286	0.0079	0.9933	0.9933
0.0012	27.0	297	0.0076	0.9933	0.9933
0.0011	28.0	308	0.0076	0.9933	0.9933
0.001	29.0	319	0.0085	0.9933	0.9933
0.0009	30.0	330	0.0088	0.9933	0.9933
0.001	31.0	341	0.0089	0.9933	0.9933
0.0009	32.0	352	0.0092	0.9933	0.9933
0.0009	33.0	363	0.0091	0.9933	0.9933
0.0008	34.0	374	0.0100	0.9933	0.9933
0.0021	35.0	385	0.0312	0.9933	0.9933
0.0008	36.0	396	0.0340	0.9933	0.9933
0.0009	37.0	407	0.0313	0.9933	0.9933
0.0008	38.0	418	0.0278	0.9933	0.9933
0.0008	39.0	429	0.0246	0.9933	0.9933
0.0008	40.0	440	0.0226	0.9933	0.9933
0.0007	41.0	451	0.0212	0.9933	0.9933
0.0007	42.0	462	0.0200	0.9933	0.9933
0.0007	43.0	473	0.0241	0.9933	0.9933
0.0007	44.0	484	0.0249	0.9933	0.9933
0.0007	45.0	495	0.0244	0.9933	0.9933
0.0007	46.0	506	0.0238	0.9933	0.9933
0.0007	47.0	517	0.0234	0.9933	0.9933
0.0006	48.0	528	0.0230	0.9933	0.9933
0.0007	49.0	539	0.0227	0.9933	0.9933
0.0007	50.0	550	0.0227	0.9933	0.9933

Framework versions

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