best_model-yelp_polarity-32-42

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: 0.5674
• Accuracy: 0.9375

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	2	0.5344	0.9219
No log	2.0	4	0.5340	0.9219
No log	3.0	6	0.5330	0.9219
No log	4.0	8	0.5309	0.9219
0.4102	5.0	10	0.5192	0.9219
0.4102	6.0	12	0.5130	0.9219
0.4102	7.0	14	0.4996	0.9219
0.4102	8.0	16	0.4697	0.9375
0.4102	9.0	18	0.4622	0.9375
0.2817	10.0	20	0.4615	0.9375
0.2817	11.0	22	0.4620	0.9375
0.2817	12.0	24	0.4612	0.9375
0.2817	13.0	26	0.4623	0.9375
0.2817	14.0	28	0.5263	0.9219
0.064	15.0	30	0.5614	0.9219
0.064	16.0	32	0.5627	0.9219
0.064	17.0	34	0.5183	0.9219
0.064	18.0	36	0.4753	0.9375
0.064	19.0	38	0.4826	0.9375
0.002	20.0	40	0.4912	0.9375
0.002	21.0	42	0.5235	0.9219
0.002	22.0	44	0.5333	0.9219
0.002	23.0	46	0.5318	0.9219
0.002	24.0	48	0.5192	0.9219
0.0001	25.0	50	0.5060	0.9375
0.0001	26.0	52	0.4997	0.9375
0.0001	27.0	54	0.4982	0.9375
0.0001	28.0	56	0.4982	0.9375
0.0001	29.0	58	0.4984	0.9375
0.0	30.0	60	0.4987	0.9375
0.0	31.0	62	0.4989	0.9375
0.0	32.0	64	0.4992	0.9375
0.0	33.0	66	0.4994	0.9375
0.0	34.0	68	0.4997	0.9375
0.0	35.0	70	0.4999	0.9375
0.0	36.0	72	0.5002	0.9375
0.0	37.0	74	0.5005	0.9375
0.0	38.0	76	0.5009	0.9375
0.0	39.0	78	0.5012	0.9375
0.0	40.0	80	0.5016	0.9375
0.0	41.0	82	0.5020	0.9375
0.0	42.0	84	0.5024	0.9375
0.0	43.0	86	0.5029	0.9375
0.0	44.0	88	0.5033	0.9375
0.0	45.0	90	0.5038	0.9375
0.0	46.0	92	0.5043	0.9375
0.0	47.0	94	0.5047	0.9375
0.0	48.0	96	0.5052	0.9375
0.0	49.0	98	0.5057	0.9375
0.0	50.0	100	0.5062	0.9375
0.0	51.0	102	0.5068	0.9375
0.0	52.0	104	0.5073	0.9375
0.0	53.0	106	0.5078	0.9375
0.0	54.0	108	0.5083	0.9375
0.0	55.0	110	0.5089	0.9375
0.0	56.0	112	0.5094	0.9375
0.0	57.0	114	0.5100	0.9375
0.0	58.0	116	0.5105	0.9375
0.0	59.0	118	0.5110	0.9375
0.0	60.0	120	0.5116	0.9375
0.0	61.0	122	0.5122	0.9375
0.0	62.0	124	0.5128	0.9375
0.0	63.0	126	0.5133	0.9375
0.0	64.0	128	0.5139	0.9375
0.0	65.0	130	0.5145	0.9375
0.0	66.0	132	0.5150	0.9375
0.0	67.0	134	0.5156	0.9375
0.0	68.0	136	0.5161	0.9375
0.0	69.0	138	0.5166	0.9375
0.0	70.0	140	0.5172	0.9375
0.0	71.0	142	0.5177	0.9375
0.0	72.0	144	0.5182	0.9375
0.0	73.0	146	0.5188	0.9375
0.0	74.0	148	0.5193	0.9375
0.0	75.0	150	0.5199	0.9375
0.0	76.0	152	0.5204	0.9375
0.0	77.0	154	0.5210	0.9375
0.0	78.0	156	0.5216	0.9375
0.0	79.0	158	0.5222	0.9375
0.0	80.0	160	0.5228	0.9375
0.0	81.0	162	0.5233	0.9375
0.0	82.0	164	0.5239	0.9375
0.0	83.0	166	0.5245	0.9375
0.0	84.0	168	0.5251	0.9375
0.0	85.0	170	0.5257	0.9375
0.0	86.0	172	0.5263	0.9375
0.0	87.0	174	0.5269	0.9375
0.0	88.0	176	0.5275	0.9375
0.0	89.0	178	0.5281	0.9375
0.0	90.0	180	0.5288	0.9375
0.0	91.0	182	0.5294	0.9375
0.0	92.0	184	0.5300	0.9375
0.0	93.0	186	0.5306	0.9375
0.0	94.0	188	0.5312	0.9375
0.0	95.0	190	0.5318	0.9375
0.0	96.0	192	0.5325	0.9375
0.0	97.0	194	0.5331	0.9375
0.0	98.0	196	0.5337	0.9375
0.0	99.0	198	0.5343	0.9375
0.0	100.0	200	0.5349	0.9375
0.0	101.0	202	0.5355	0.9375
0.0	102.0	204	0.5362	0.9375
0.0	103.0	206	0.5368	0.9375
0.0	104.0	208	0.5374	0.9375
0.0	105.0	210	0.5381	0.9375
0.0	106.0	212	0.5387	0.9375
0.0	107.0	214	0.5394	0.9375
0.0	108.0	216	0.5400	0.9375
0.0	109.0	218	0.5407	0.9375
0.0	110.0	220	0.5413	0.9375
0.0	111.0	222	0.5419	0.9375
0.0	112.0	224	0.5425	0.9375
0.0	113.0	226	0.5432	0.9375
0.0	114.0	228	0.5438	0.9375
0.0	115.0	230	0.5444	0.9375
0.0	116.0	232	0.5450	0.9375
0.0	117.0	234	0.5457	0.9375
0.0	118.0	236	0.5463	0.9375
0.0	119.0	238	0.5469	0.9375
0.0	120.0	240	0.5476	0.9375
0.0	121.0	242	0.5482	0.9375
0.0	122.0	244	0.5489	0.9375
0.0	123.0	246	0.5495	0.9375
0.0	124.0	248	0.5502	0.9375
0.0	125.0	250	0.5509	0.9375
0.0	126.0	252	0.5516	0.9375
0.0	127.0	254	0.5522	0.9375
0.0	128.0	256	0.5529	0.9375
0.0	129.0	258	0.5536	0.9375
0.0	130.0	260	0.5543	0.9375
0.0	131.0	262	0.5549	0.9375
0.0	132.0	264	0.5556	0.9375
0.0	133.0	266	0.5563	0.9375
0.0	134.0	268	0.5570	0.9375
0.0	135.0	270	0.5576	0.9375
0.0	136.0	272	0.5583	0.9375
0.0	137.0	274	0.5590	0.9375
0.0	138.0	276	0.5597	0.9375
0.0	139.0	278	0.5603	0.9375
0.0	140.0	280	0.5610	0.9375
0.0	141.0	282	0.5616	0.9375
0.0	142.0	284	0.5623	0.9375
0.0	143.0	286	0.5629	0.9375
0.0	144.0	288	0.5635	0.9375
0.0	145.0	290	0.5642	0.9375
0.0	146.0	292	0.5648	0.9375
0.0	147.0	294	0.5655	0.9375
0.0	148.0	296	0.5661	0.9375
0.0	149.0	298	0.5667	0.9375
0.0	150.0	300	0.5674	0.9375

Framework versions

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