best_model-sst-2-16-87

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

• Loss: 0.6392
• Accuracy: 0.875

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: 1e-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
• lr_scheduler_warmup_steps: 500
• num_epochs: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	1	0.5816	0.8438
No log	2.0	2	0.5813	0.8438
No log	3.0	3	0.5807	0.8438
No log	4.0	4	0.5798	0.8438
No log	5.0	5	0.5786	0.8438
No log	6.0	6	0.5770	0.8438
No log	7.0	7	0.5750	0.8438
No log	8.0	8	0.5726	0.8438
No log	9.0	9	0.5701	0.8438
0.4546	10.0	10	0.5672	0.8438
0.4546	11.0	11	0.5641	0.8438
0.4546	12.0	12	0.5614	0.8438
0.4546	13.0	13	0.5586	0.8438
0.4546	14.0	14	0.5560	0.8438
0.4546	15.0	15	0.5530	0.8438
0.4546	16.0	16	0.5501	0.8438
0.4546	17.0	17	0.5470	0.8438
0.4546	18.0	18	0.5438	0.8438
0.4546	19.0	19	0.5407	0.8438
0.4413	20.0	20	0.5369	0.8438
0.4413	21.0	21	0.5325	0.8438
0.4413	22.0	22	0.5280	0.8438
0.4413	23.0	23	0.5230	0.8438
0.4413	24.0	24	0.5180	0.8438
0.4413	25.0	25	0.5132	0.8438
0.4413	26.0	26	0.5088	0.8438
0.4413	27.0	27	0.5049	0.8438
0.4413	28.0	28	0.5014	0.8438
0.4413	29.0	29	0.4985	0.8438
0.3899	30.0	30	0.4964	0.8438
0.3899	31.0	31	0.4951	0.8438
0.3899	32.0	32	0.4937	0.8438
0.3899	33.0	33	0.4919	0.8438
0.3899	34.0	34	0.4902	0.8438
0.3899	35.0	35	0.4884	0.8438
0.3899	36.0	36	0.4870	0.8438
0.3899	37.0	37	0.4854	0.8438
0.3899	38.0	38	0.4844	0.8438
0.3899	39.0	39	0.4832	0.875
0.3672	40.0	40	0.4821	0.875
0.3672	41.0	41	0.4817	0.875
0.3672	42.0	42	0.4817	0.875
0.3672	43.0	43	0.4820	0.875
0.3672	44.0	44	0.4830	0.875
0.3672	45.0	45	0.4838	0.875
0.3672	46.0	46	0.4848	0.875
0.3672	47.0	47	0.4855	0.875
0.3672	48.0	48	0.4854	0.875
0.3672	49.0	49	0.4860	0.875
0.2765	50.0	50	0.4872	0.875
0.2765	51.0	51	0.4878	0.875
0.2765	52.0	52	0.4892	0.875
0.2765	53.0	53	0.4913	0.875
0.2765	54.0	54	0.4942	0.8438
0.2765	55.0	55	0.4977	0.8438
0.2765	56.0	56	0.5017	0.8438
0.2765	57.0	57	0.5074	0.8438
0.2765	58.0	58	0.5148	0.8438
0.2765	59.0	59	0.5211	0.8438
0.2106	60.0	60	0.5286	0.8438
0.2106	61.0	61	0.5361	0.8438
0.2106	62.0	62	0.5429	0.8438
0.2106	63.0	63	0.5497	0.8438
0.2106	64.0	64	0.5551	0.8438
0.2106	65.0	65	0.5569	0.8438
0.2106	66.0	66	0.5556	0.8438
0.2106	67.0	67	0.5522	0.8438
0.2106	68.0	68	0.5465	0.8438
0.2106	69.0	69	0.5400	0.8438
0.1587	70.0	70	0.5359	0.8438
0.1587	71.0	71	0.5311	0.8438
0.1587	72.0	72	0.5252	0.8438
0.1587	73.0	73	0.5217	0.8438
0.1587	74.0	74	0.5192	0.8438
0.1587	75.0	75	0.5158	0.8438
0.1587	76.0	76	0.5128	0.8438
0.1587	77.0	77	0.5113	0.8438
0.1587	78.0	78	0.5105	0.8438
0.1587	79.0	79	0.5091	0.8438
0.122	80.0	80	0.5090	0.8438
0.122	81.0	81	0.5100	0.8438
0.122	82.0	82	0.5126	0.8438
0.122	83.0	83	0.5167	0.8438
0.122	84.0	84	0.5215	0.8438
0.122	85.0	85	0.5274	0.8438
0.122	86.0	86	0.5351	0.8438
0.122	87.0	87	0.5439	0.8438
0.122	88.0	88	0.5547	0.8438
0.122	89.0	89	0.5658	0.8438
0.0738	90.0	90	0.5778	0.8438
0.0738	91.0	91	0.5872	0.8438
0.0738	92.0	92	0.5963	0.8438
0.0738	93.0	93	0.6027	0.8438
0.0738	94.0	94	0.6059	0.8438
0.0738	95.0	95	0.6070	0.8438
0.0738	96.0	96	0.6052	0.8438
0.0738	97.0	97	0.6020	0.8438
0.0738	98.0	98	0.5950	0.8438
0.0738	99.0	99	0.5870	0.8438
0.0328	100.0	100	0.5788	0.8438
0.0328	101.0	101	0.5706	0.8438
0.0328	102.0	102	0.5638	0.8438
0.0328	103.0	103	0.5578	0.8438
0.0328	104.0	104	0.5530	0.8438
0.0328	105.0	105	0.5491	0.875
0.0328	106.0	106	0.5465	0.875
0.0328	107.0	107	0.5457	0.875
0.0328	108.0	108	0.5456	0.875
0.0328	109.0	109	0.5462	0.875
0.0221	110.0	110	0.5473	0.875
0.0221	111.0	111	0.5486	0.875
0.0221	112.0	112	0.5500	0.875
0.0221	113.0	113	0.5521	0.875
0.0221	114.0	114	0.5543	0.875
0.0221	115.0	115	0.5564	0.875
0.0221	116.0	116	0.5589	0.875
0.0221	117.0	117	0.5613	0.875
0.0221	118.0	118	0.5637	0.875
0.0221	119.0	119	0.5660	0.875
0.017	120.0	120	0.5682	0.875
0.017	121.0	121	0.5704	0.875
0.017	122.0	122	0.5727	0.875
0.017	123.0	123	0.5748	0.875
0.017	124.0	124	0.5772	0.875
0.017	125.0	125	0.5796	0.875
0.017	126.0	126	0.5820	0.875
0.017	127.0	127	0.5847	0.875
0.017	128.0	128	0.5874	0.875
0.017	129.0	129	0.5900	0.875
0.0129	130.0	130	0.5926	0.875
0.0129	131.0	131	0.5951	0.875
0.0129	132.0	132	0.5976	0.875
0.0129	133.0	133	0.6001	0.875
0.0129	134.0	134	0.6027	0.875
0.0129	135.0	135	0.6051	0.875
0.0129	136.0	136	0.6076	0.875
0.0129	137.0	137	0.6099	0.875
0.0129	138.0	138	0.6123	0.875
0.0129	139.0	139	0.6146	0.875
0.0103	140.0	140	0.6169	0.875
0.0103	141.0	141	0.6192	0.875
0.0103	142.0	142	0.6216	0.875
0.0103	143.0	143	0.6239	0.875
0.0103	144.0	144	0.6261	0.875
0.0103	145.0	145	0.6284	0.875
0.0103	146.0	146	0.6306	0.875
0.0103	147.0	147	0.6328	0.875
0.0103	148.0	148	0.6350	0.875
0.0103	149.0	149	0.6371	0.875
0.0084	150.0	150	0.6392	0.875

Framework versions

• Transformers 4.32.0.dev0
• Pytorch 2.0.1+cu118
• Datasets 2.4.0
• Tokenizers 0.13.3