best_model-yelp_polarity-64-87

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: 0.5142
• Accuracy: 0.9219

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	4	0.4813	0.9453
No log	2.0	8	0.4523	0.9531
0.996	3.0	12	0.4366	0.9453
0.996	4.0	16	0.4239	0.9531
0.8647	5.0	20	0.4191	0.9531
0.8647	6.0	24	0.4066	0.9531
0.8647	7.0	28	0.4268	0.9531
0.6876	8.0	32	0.5275	0.9453
0.6876	9.0	36	0.6025	0.9453
0.5833	10.0	40	0.6144	0.9453
0.5833	11.0	44	0.6062	0.9453
0.5833	12.0	48	0.5946	0.9453
0.4071	13.0	52	0.5677	0.9453
0.4071	14.0	56	0.5733	0.9453
0.2545	15.0	60	0.5830	0.9453
0.2545	16.0	64	0.5768	0.9453
0.2545	17.0	68	0.5639	0.9453
0.1255	18.0	72	0.5467	0.9453
0.1255	19.0	76	0.5185	0.9453
0.1119	20.0	80	0.4410	0.9453
0.1119	21.0	84	0.4174	0.9531
0.1119	22.0	88	0.4014	0.9453
0.0568	23.0	92	0.4155	0.9531
0.0568	24.0	96	0.4084	0.9375
0.0295	25.0	100	0.3999	0.9297
0.0295	26.0	104	0.4070	0.9219
0.0295	27.0	108	0.4131	0.9219
0.0226	28.0	112	0.4255	0.9219
0.0226	29.0	116	0.4287	0.9219
0.0197	30.0	120	0.4395	0.9297
0.0197	31.0	124	0.4473	0.9297
0.0197	32.0	128	0.4604	0.9297
0.0161	33.0	132	0.4653	0.9297
0.0161	34.0	136	0.4682	0.9297
0.0114	35.0	140	0.4805	0.9297
0.0114	36.0	144	0.4598	0.9297
0.0114	37.0	148	0.4290	0.9297
0.0054	38.0	152	0.4322	0.9297
0.0054	39.0	156	0.4623	0.9219
0.0039	40.0	160	0.4877	0.9297
0.0039	41.0	164	0.4887	0.9297
0.0039	42.0	168	0.4805	0.9297
0.0003	43.0	172	0.4766	0.9219
0.0003	44.0	176	0.4759	0.9297
0.0	45.0	180	0.4779	0.9297
0.0	46.0	184	0.4799	0.9219
0.0	47.0	188	0.4816	0.9219
0.0	48.0	192	0.4829	0.9219
0.0	49.0	196	0.4841	0.9219
0.0	50.0	200	0.4850	0.9219
0.0	51.0	204	0.4859	0.9219
0.0	52.0	208	0.4867	0.9219
0.0	53.0	212	0.4873	0.9219
0.0	54.0	216	0.4879	0.9219
0.0	55.0	220	0.4883	0.9219
0.0	56.0	224	0.4887	0.9219
0.0	57.0	228	0.4890	0.9219
0.0	58.0	232	0.4894	0.9219
0.0	59.0	236	0.4896	0.9219
0.0	60.0	240	0.4899	0.9219
0.0	61.0	244	0.4903	0.9219
0.0	62.0	248	0.4907	0.9219
0.0	63.0	252	0.4912	0.9219
0.0	64.0	256	0.4916	0.9219
0.0	65.0	260	0.4920	0.9219
0.0	66.0	264	0.4924	0.9219
0.0	67.0	268	0.4927	0.9219
0.0	68.0	272	0.4931	0.9219
0.0	69.0	276	0.4934	0.9219
0.0	70.0	280	0.4938	0.9219
0.0	71.0	284	0.4943	0.9219
0.0	72.0	288	0.4945	0.9219
0.0	73.0	292	0.4949	0.9219
0.0	74.0	296	0.4953	0.9219
0.0	75.0	300	0.4955	0.9219
0.0	76.0	304	0.4959	0.9219
0.0	77.0	308	0.4962	0.9219
0.0	78.0	312	0.4965	0.9219
0.0	79.0	316	0.4970	0.9219
0.0	80.0	320	0.4975	0.9219
0.0	81.0	324	0.4978	0.9219
0.0	82.0	328	0.4982	0.9219
0.0	83.0	332	0.4985	0.9219
0.0	84.0	336	0.4987	0.9219
0.0	85.0	340	0.4988	0.9219
0.0	86.0	344	0.4990	0.9219
0.0	87.0	348	0.4993	0.9219
0.0	88.0	352	0.4994	0.9219
0.0	89.0	356	0.4996	0.9219
0.0	90.0	360	0.4998	0.9219
0.0	91.0	364	0.5001	0.9219
0.0	92.0	368	0.5004	0.9219
0.0	93.0	372	0.5006	0.9219
0.0	94.0	376	0.5009	0.9219
0.0	95.0	380	0.5012	0.9219
0.0	96.0	384	0.5013	0.9219
0.0	97.0	388	0.5017	0.9219
0.0	98.0	392	0.5021	0.9219
0.0	99.0	396	0.5021	0.9219
0.0	100.0	400	0.5022	0.9219
0.0	101.0	404	0.5025	0.9219
0.0	102.0	408	0.5029	0.9219
0.0	103.0	412	0.5030	0.9219
0.0	104.0	416	0.5033	0.9219
0.0	105.0	420	0.5037	0.9219
0.0	106.0	424	0.5040	0.9219
0.0	107.0	428	0.5044	0.9219
0.0	108.0	432	0.5046	0.9219
0.0	109.0	436	0.5047	0.9219
0.0	110.0	440	0.5050	0.9219
0.0	111.0	444	0.5053	0.9219
0.0	112.0	448	0.5057	0.9219
0.0	113.0	452	0.5061	0.9219
0.0	114.0	456	0.5065	0.9219
0.0	115.0	460	0.5070	0.9219
0.0	116.0	464	0.5073	0.9219
0.0	117.0	468	0.5077	0.9219
0.0	118.0	472	0.5080	0.9219
0.0	119.0	476	0.5082	0.9219
0.0	120.0	480	0.5085	0.9219
0.0	121.0	484	0.5087	0.9219
0.0	122.0	488	0.5090	0.9219
0.0	123.0	492	0.5095	0.9219
0.0	124.0	496	0.5098	0.9219
0.0	125.0	500	0.5102	0.9219
0.0	126.0	504	0.5107	0.9219
0.0	127.0	508	0.5111	0.9219
0.0	128.0	512	0.5115	0.9219
0.0	129.0	516	0.5118	0.9219
0.0	130.0	520	0.5120	0.9219
0.0	131.0	524	0.5122	0.9219
0.0	132.0	528	0.5126	0.9219
0.0	133.0	532	0.5127	0.9219
0.0	134.0	536	0.5129	0.9219
0.0	135.0	540	0.5131	0.9219
0.0	136.0	544	0.5132	0.9219
0.0	137.0	548	0.5134	0.9219
0.0	138.0	552	0.5135	0.9219
0.0	139.0	556	0.5136	0.9219
0.0	140.0	560	0.5137	0.9219
0.0	141.0	564	0.5138	0.9219
0.0	142.0	568	0.5139	0.9219
0.0	143.0	572	0.5140	0.9219
0.0	144.0	576	0.5140	0.9219
0.0	145.0	580	0.5141	0.9219
0.0	146.0	584	0.5141	0.9219
0.0	147.0	588	0.5141	0.9219
0.0	148.0	592	0.5142	0.9219
0.0	149.0	596	0.5142	0.9219
0.0	150.0	600	0.5142	0.9219

Framework versions

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