best_model-yelp_polarity-16-13

This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3928
• 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	0.7228	0.5
No log	2.0	2	0.7227	0.5
No log	3.0	3	0.7227	0.5
No log	4.0	4	0.7225	0.5
No log	5.0	5	0.7224	0.5
No log	6.0	6	0.7221	0.5
No log	7.0	7	0.7219	0.5
No log	8.0	8	0.7216	0.5
No log	9.0	9	0.7213	0.5
0.7034	10.0	10	0.7209	0.5
0.7034	11.0	11	0.7205	0.5
0.7034	12.0	12	0.7200	0.5
0.7034	13.0	13	0.7195	0.5
0.7034	14.0	14	0.7189	0.5
0.7034	15.0	15	0.7183	0.5
0.7034	16.0	16	0.7177	0.5
0.7034	17.0	17	0.7170	0.5
0.7034	18.0	18	0.7163	0.5
0.7034	19.0	19	0.7156	0.5
0.6925	20.0	20	0.7148	0.5
0.6925	21.0	21	0.7140	0.5
0.6925	22.0	22	0.7132	0.5
0.6925	23.0	23	0.7123	0.5
0.6925	24.0	24	0.7113	0.5
0.6925	25.0	25	0.7104	0.5
0.6925	26.0	26	0.7093	0.5
0.6925	27.0	27	0.7082	0.5
0.6925	28.0	28	0.7071	0.5
0.6925	29.0	29	0.7059	0.5
0.6581	30.0	30	0.7047	0.5
0.6581	31.0	31	0.7034	0.5
0.6581	32.0	32	0.7021	0.5
0.6581	33.0	33	0.7007	0.5
0.6581	34.0	34	0.6991	0.5
0.6581	35.0	35	0.6975	0.5
0.6581	36.0	36	0.6958	0.5
0.6581	37.0	37	0.6941	0.5
0.6581	38.0	38	0.6923	0.5
0.6581	39.0	39	0.6904	0.5
0.6325	40.0	40	0.6883	0.5
0.6325	41.0	41	0.6862	0.5
0.6325	42.0	42	0.6841	0.5
0.6325	43.0	43	0.6818	0.5
0.6325	44.0	44	0.6794	0.5
0.6325	45.0	45	0.6770	0.5
0.6325	46.0	46	0.6745	0.5312
0.6325	47.0	47	0.6718	0.5312
0.6325	48.0	48	0.6690	0.5312
0.6325	49.0	49	0.6662	0.5625
0.573	50.0	50	0.6633	0.5625
0.573	51.0	51	0.6602	0.5625
0.573	52.0	52	0.6571	0.5625
0.573	53.0	53	0.6538	0.5625
0.573	54.0	54	0.6504	0.5625
0.573	55.0	55	0.6469	0.5625
0.573	56.0	56	0.6435	0.5625
0.573	57.0	57	0.6401	0.625
0.573	58.0	58	0.6368	0.625
0.573	59.0	59	0.6336	0.6562
0.5136	60.0	60	0.6305	0.6875
0.5136	61.0	61	0.6273	0.6562
0.5136	62.0	62	0.6240	0.6562
0.5136	63.0	63	0.6206	0.6562
0.5136	64.0	64	0.6172	0.6875
0.5136	65.0	65	0.6138	0.6875
0.5136	66.0	66	0.6105	0.6875
0.5136	67.0	67	0.6072	0.6875
0.5136	68.0	68	0.6038	0.6875
0.5136	69.0	69	0.6004	0.6875
0.4388	70.0	70	0.5968	0.6875
0.4388	71.0	71	0.5931	0.7188
0.4388	72.0	72	0.5893	0.75
0.4388	73.0	73	0.5854	0.75
0.4388	74.0	74	0.5814	0.75
0.4388	75.0	75	0.5773	0.75
0.4388	76.0	76	0.5732	0.75
0.4388	77.0	77	0.5695	0.7812
0.4388	78.0	78	0.5660	0.7812
0.4388	79.0	79	0.5626	0.7812
0.3545	80.0	80	0.5590	0.7812
0.3545	81.0	81	0.5553	0.7812
0.3545	82.0	82	0.5514	0.8125
0.3545	83.0	83	0.5476	0.7812
0.3545	84.0	84	0.5437	0.7812
0.3545	85.0	85	0.5396	0.7812
0.3545	86.0	86	0.5358	0.7812
0.3545	87.0	87	0.5316	0.7812
0.3545	88.0	88	0.5277	0.7812
0.3545	89.0	89	0.5238	0.7812
0.2725	90.0	90	0.5197	0.7812
0.2725	91.0	91	0.5159	0.7812
0.2725	92.0	92	0.5120	0.7812
0.2725	93.0	93	0.5079	0.7812
0.2725	94.0	94	0.5034	0.7812
0.2725	95.0	95	0.4983	0.7812
0.2725	96.0	96	0.4934	0.7812
0.2725	97.0	97	0.4885	0.7812
0.2725	98.0	98	0.4835	0.7812
0.2725	99.0	99	0.4790	0.8125
0.199	100.0	100	0.4751	0.8125
0.199	101.0	101	0.4714	0.8125
0.199	102.0	102	0.4677	0.8125
0.199	103.0	103	0.4634	0.8438
0.199	104.0	104	0.4585	0.8438
0.199	105.0	105	0.4532	0.875
0.199	106.0	106	0.4484	0.875
0.199	107.0	107	0.4439	0.875
0.199	108.0	108	0.4400	0.875
0.199	109.0	109	0.4363	0.875
0.1406	110.0	110	0.4329	0.875
0.1406	111.0	111	0.4296	0.875
0.1406	112.0	112	0.4259	0.875
0.1406	113.0	113	0.4219	0.8438
0.1406	114.0	114	0.4176	0.8438
0.1406	115.0	115	0.4138	0.8438
0.1406	116.0	116	0.4108	0.8438
0.1406	117.0	117	0.4077	0.8438
0.1406	118.0	118	0.4042	0.8438
0.1406	119.0	119	0.4003	0.8438
0.0921	120.0	120	0.3968	0.8438
0.0921	121.0	121	0.3936	0.8438
0.0921	122.0	122	0.3905	0.8438
0.0921	123.0	123	0.3878	0.8438
0.0921	124.0	124	0.3851	0.8438
0.0921	125.0	125	0.3823	0.8438
0.0921	126.0	126	0.3802	0.8438
0.0921	127.0	127	0.3786	0.8438
0.0921	128.0	128	0.3769	0.8125
0.0921	129.0	129	0.3748	0.8125
0.0543	130.0	130	0.3721	0.8125
0.0543	131.0	131	0.3700	0.8125
0.0543	132.0	132	0.3685	0.8125
0.0543	133.0	133	0.3687	0.8125
0.0543	134.0	134	0.3699	0.8125
0.0543	135.0	135	0.3711	0.8125
0.0543	136.0	136	0.3719	0.8125
0.0543	137.0	137	0.3716	0.8125
0.0543	138.0	138	0.3706	0.8438
0.0543	139.0	139	0.3699	0.8438
0.0313	140.0	140	0.3692	0.875
0.0313	141.0	141	0.3690	0.875
0.0313	142.0	142	0.3690	0.875
0.0313	143.0	143	0.3698	0.875
0.0313	144.0	144	0.3715	0.875
0.0313	145.0	145	0.3737	0.875
0.0313	146.0	146	0.3766	0.875
0.0313	147.0	147	0.3798	0.875
0.0313	148.0	148	0.3838	0.875
0.0313	149.0	149	0.3884	0.875
0.0183	150.0	150	0.3928	0.875

Framework versions

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