best_model-yelp_polarity-32-87

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: 0.5929
• 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	0.4518	0.9531
No log	2.0	4	0.4575	0.9531
No log	3.0	6	0.4656	0.9531
No log	4.0	8	0.4755	0.9531
0.3146	5.0	10	0.5047	0.9375
0.3146	6.0	12	0.5491	0.9375
0.3146	7.0	14	0.5854	0.9375
0.3146	8.0	16	0.6060	0.9375
0.3146	9.0	18	0.6187	0.9375
0.2346	10.0	20	0.6304	0.9375
0.2346	11.0	22	0.6336	0.9375
0.2346	12.0	24	0.6359	0.9375
0.2346	13.0	26	0.6345	0.9375
0.2346	14.0	28	0.6333	0.9375
0.0633	15.0	30	0.6349	0.9375
0.0633	16.0	32	0.6359	0.9375
0.0633	17.0	34	0.6298	0.9375
0.0633	18.0	36	0.6191	0.9375
0.0633	19.0	38	0.6057	0.9375
0.0003	20.0	40	0.5963	0.9375
0.0003	21.0	42	0.5988	0.9375
0.0003	22.0	44	0.6050	0.9375
0.0003	23.0	46	0.6098	0.9375
0.0003	24.0	48	0.6134	0.9375
0.0	25.0	50	0.6160	0.9375
0.0	26.0	52	0.6177	0.9375
0.0	27.0	54	0.6187	0.9375
0.0	28.0	56	0.6190	0.9375
0.0	29.0	58	0.6189	0.9375
0.0	30.0	60	0.6186	0.9375
0.0	31.0	62	0.6179	0.9375
0.0	32.0	64	0.6172	0.9375
0.0	33.0	66	0.6161	0.9375
0.0	34.0	68	0.6151	0.9375
0.0	35.0	70	0.6140	0.9375
0.0	36.0	72	0.6128	0.9375
0.0	37.0	74	0.6116	0.9375
0.0	38.0	76	0.6104	0.9375
0.0	39.0	78	0.6091	0.9375
0.0	40.0	80	0.6079	0.9375
0.0	41.0	82	0.6066	0.9375
0.0	42.0	84	0.6054	0.9375
0.0	43.0	86	0.6041	0.9375
0.0	44.0	88	0.6029	0.9375
0.0	45.0	90	0.6018	0.9375
0.0	46.0	92	0.6006	0.9375
0.0	47.0	94	0.5996	0.9375
0.0	48.0	96	0.5986	0.9375
0.0	49.0	98	0.5977	0.9375
0.0	50.0	100	0.5968	0.9375
0.0	51.0	102	0.5959	0.9375
0.0	52.0	104	0.5951	0.9375
0.0	53.0	106	0.5943	0.9375
0.0	54.0	108	0.5937	0.9375
0.0	55.0	110	0.5931	0.9375
0.0	56.0	112	0.5925	0.9375
0.0	57.0	114	0.5918	0.9375
0.0	58.0	116	0.5912	0.9375
0.0	59.0	118	0.5906	0.9375
0.0	60.0	120	0.5899	0.9375
0.0	61.0	122	0.5894	0.9375
0.0	62.0	124	0.5890	0.9375
0.0	63.0	126	0.5886	0.9375
0.0	64.0	128	0.5881	0.9375
0.0	65.0	130	0.5878	0.9375
0.0	66.0	132	0.5874	0.9375
0.0	67.0	134	0.5872	0.9375
0.0	68.0	136	0.5871	0.9375
0.0	69.0	138	0.5872	0.9375
0.0	70.0	140	0.5871	0.9375
0.0	71.0	142	0.5872	0.9375
0.0	72.0	144	0.5872	0.9375
0.0	73.0	146	0.5872	0.9375
0.0	74.0	148	0.5873	0.9375
0.0	75.0	150	0.5873	0.9375
0.0	76.0	152	0.5875	0.9375
0.0	77.0	154	0.5875	0.9375
0.0	78.0	156	0.5876	0.9375
0.0	79.0	158	0.5877	0.9375
0.0	80.0	160	0.5879	0.9375
0.0	81.0	162	0.5881	0.9375
0.0	82.0	164	0.5883	0.9375
0.0	83.0	166	0.5884	0.9375
0.0	84.0	168	0.5885	0.9375
0.0	85.0	170	0.5887	0.9375
0.0	86.0	172	0.5887	0.9375
0.0	87.0	174	0.5886	0.9375
0.0	88.0	176	0.5888	0.9375
0.0	89.0	178	0.5887	0.9375
0.0	90.0	180	0.5885	0.9375
0.0	91.0	182	0.5884	0.9375
0.0	92.0	184	0.5882	0.9375
0.0	93.0	186	0.5880	0.9375
0.0	94.0	188	0.5879	0.9375
0.0	95.0	190	0.5878	0.9375
0.0	96.0	192	0.5876	0.9375
0.0	97.0	194	0.5874	0.9375
0.0	98.0	196	0.5873	0.9375
0.0	99.0	198	0.5872	0.9375
0.0	100.0	200	0.5870	0.9375
0.0	101.0	202	0.5870	0.9375
0.0	102.0	204	0.5870	0.9375
0.0	103.0	206	0.5868	0.9375
0.0	104.0	208	0.5866	0.9375
0.0	105.0	210	0.5866	0.9375
0.0	106.0	212	0.5867	0.9375
0.0	107.0	214	0.5867	0.9375
0.0	108.0	216	0.5867	0.9375
0.0	109.0	218	0.5867	0.9375
0.0	110.0	220	0.5869	0.9375
0.0	111.0	222	0.5870	0.9375
0.0	112.0	224	0.5870	0.9375
0.0	113.0	226	0.5872	0.9375
0.0	114.0	228	0.5877	0.9375
0.0	115.0	230	0.5881	0.9375
0.0	116.0	232	0.5885	0.9375
0.0	117.0	234	0.5888	0.9375
0.0	118.0	236	0.5891	0.9375
0.0	119.0	238	0.5894	0.9375
0.0	120.0	240	0.5897	0.9375
0.0	121.0	242	0.5899	0.9375
0.0	122.0	244	0.5900	0.9375
0.0	123.0	246	0.5901	0.9375
0.0	124.0	248	0.5903	0.9375
0.0	125.0	250	0.5903	0.9375
0.0	126.0	252	0.5906	0.9375
0.0	127.0	254	0.5908	0.9375
0.0	128.0	256	0.5909	0.9375
0.0	129.0	258	0.5911	0.9375
0.0	130.0	260	0.5914	0.9375
0.0	131.0	262	0.5916	0.9375
0.0	132.0	264	0.5918	0.9375
0.0	133.0	266	0.5922	0.9375
0.0	134.0	268	0.5922	0.9375
0.0	135.0	270	0.5924	0.9375
0.0	136.0	272	0.5925	0.9375
0.0	137.0	274	0.5923	0.9375
0.0	138.0	276	0.5923	0.9375
0.0	139.0	278	0.5922	0.9375
0.0	140.0	280	0.5920	0.9375
0.0	141.0	282	0.5921	0.9375
0.0	142.0	284	0.5922	0.9375
0.0	143.0	286	0.5921	0.9375
0.0	144.0	288	0.5921	0.9375
0.0	145.0	290	0.5922	0.9375
0.0	146.0	292	0.5924	0.9375
0.0	147.0	294	0.5926	0.9375
0.0	148.0	296	0.5927	0.9375
0.0	149.0	298	0.5929	0.9375
0.0	150.0	300	0.5929	0.9375

Framework versions

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