best_model-yelp_polarity-64-21

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: 0.6300
• 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	0.6653	0.9297
No log	2.0	8	0.6599	0.9375
0.3506	3.0	12	0.6517	0.9375
0.3506	4.0	16	0.6448	0.9375
0.4992	5.0	20	0.6507	0.9375
0.4992	6.0	24	0.6967	0.9219
0.4992	7.0	28	0.7602	0.9141
0.3039	8.0	32	0.9351	0.8984
0.3039	9.0	36	0.9244	0.8984
0.2241	10.0	40	0.7974	0.9062
0.2241	11.0	44	0.7229	0.9219
0.2241	12.0	48	0.6981	0.9219
0.1025	13.0	52	0.6961	0.9219
0.1025	14.0	56	0.6819	0.9219
0.1057	15.0	60	0.6655	0.9219
0.1057	16.0	64	0.6463	0.9219
0.1057	17.0	68	0.6240	0.9219
0.0733	18.0	72	0.6086	0.9141
0.0733	19.0	76	0.6109	0.9141
0.0366	20.0	80	0.6219	0.9141
0.0366	21.0	84	0.6291	0.9141
0.0366	22.0	88	0.6227	0.9219
0.0449	23.0	92	0.6182	0.9219
0.0449	24.0	96	0.6148	0.9219
0.0188	25.0	100	0.5999	0.9219
0.0188	26.0	104	0.5537	0.9297
0.0188	27.0	108	0.5538	0.9297
0.0146	28.0	112	0.5492	0.9297
0.0146	29.0	116	0.5275	0.9297
0.0131	30.0	120	0.5212	0.9219
0.0131	31.0	124	0.5486	0.9219
0.0131	32.0	128	0.5641	0.9141
0.0074	33.0	132	0.5489	0.9219
0.0074	34.0	136	0.5426	0.9219
0.0042	35.0	140	0.5468	0.9141
0.0042	36.0	144	0.5411	0.9141
0.0042	37.0	148	0.5366	0.9219
0.0027	38.0	152	0.5306	0.9219
0.0027	39.0	156	0.5182	0.9219
0.0011	40.0	160	0.5096	0.9219
0.0011	41.0	164	0.5059	0.9219
0.0011	42.0	168	0.5130	0.9219
0.0007	43.0	172	0.5198	0.9219
0.0007	44.0	176	0.5172	0.9219
0.0007	45.0	180	0.5129	0.9219
0.0007	46.0	184	0.5337	0.9062
0.0007	47.0	188	0.5600	0.9141
0.0003	48.0	192	0.5687	0.9141
0.0003	49.0	196	0.5413	0.9141
0.0003	50.0	200	0.5270	0.9062
0.0003	51.0	204	0.5249	0.9141
0.0003	52.0	208	0.5315	0.9141
0.0002	53.0	212	0.5528	0.9141
0.0002	54.0	216	0.5732	0.9141
0.0001	55.0	220	0.5812	0.9141
0.0001	56.0	224	0.5871	0.9141
0.0001	57.0	228	0.5854	0.9141
0.0001	58.0	232	0.5846	0.9141
0.0001	59.0	236	0.5842	0.9141
0.0	60.0	240	0.5865	0.9141
0.0	61.0	244	0.5895	0.9141
0.0	62.0	248	0.5908	0.9141
0.0001	63.0	252	0.5911	0.9141
0.0001	64.0	256	0.5905	0.9141
0.0	65.0	260	0.5870	0.9141
0.0	66.0	264	0.5859	0.9141
0.0	67.0	268	0.5863	0.9141
0.0	68.0	272	0.5881	0.9141
0.0	69.0	276	0.5888	0.9141
0.0	70.0	280	0.5902	0.9141
0.0	71.0	284	0.5926	0.9141
0.0	72.0	288	0.5945	0.9141
0.0	73.0	292	0.5949	0.9141
0.0	74.0	296	0.5962	0.9141
0.0	75.0	300	0.5982	0.9141
0.0	76.0	304	0.6003	0.9141
0.0	77.0	308	0.6014	0.9141
0.0	78.0	312	0.6018	0.9219
0.0	79.0	316	0.6024	0.9219
0.0	80.0	320	0.6037	0.9219
0.0	81.0	324	0.6041	0.9219
0.0	82.0	328	0.6052	0.9219
0.0	83.0	332	0.6064	0.9219
0.0	84.0	336	0.6069	0.9219
0.0	85.0	340	0.6069	0.9219
0.0	86.0	344	0.6074	0.9219
0.0	87.0	348	0.6089	0.9219
0.0	88.0	352	0.6098	0.9219
0.0	89.0	356	0.6098	0.9219
0.0	90.0	360	0.6100	0.9219
0.0	91.0	364	0.6098	0.9219
0.0	92.0	368	0.6098	0.9219
0.0	93.0	372	0.6101	0.9219
0.0	94.0	376	0.6111	0.9219
0.0	95.0	380	0.6122	0.9219
0.0	96.0	384	0.6131	0.9219
0.0	97.0	388	0.6122	0.9219
0.0	98.0	392	0.6127	0.9219
0.0	99.0	396	0.6124	0.9219
0.0	100.0	400	0.6120	0.9219
0.0	101.0	404	0.6127	0.9219
0.0	102.0	408	0.6132	0.9219
0.0	103.0	412	0.6140	0.9219
0.0	104.0	416	0.6150	0.9219
0.0	105.0	420	0.6158	0.9219
0.0	106.0	424	0.6160	0.9219
0.0	107.0	428	0.6161	0.9219
0.0	108.0	432	0.6166	0.9219
0.0	109.0	436	0.6168	0.9219
0.0	110.0	440	0.6170	0.9219
0.0	111.0	444	0.6178	0.9219
0.0	112.0	448	0.6184	0.9219
0.0	113.0	452	0.6189	0.9219
0.0	114.0	456	0.6197	0.9219
0.0	115.0	460	0.6213	0.9219
0.0	116.0	464	0.6220	0.9219
0.0	117.0	468	0.6226	0.9219
0.0	118.0	472	0.6229	0.9219
0.0	119.0	476	0.6235	0.9219
0.0	120.0	480	0.6219	0.9219
0.0	121.0	484	0.6219	0.9219
0.0	122.0	488	0.6223	0.9219
0.0	123.0	492	0.6236	0.9219
0.0	124.0	496	0.6246	0.9219
0.0	125.0	500	0.6259	0.9219
0.0	126.0	504	0.6265	0.9219
0.0	127.0	508	0.6270	0.9219
0.0	128.0	512	0.6272	0.9219
0.0	129.0	516	0.6271	0.9219
0.0	130.0	520	0.6262	0.9219
0.0	131.0	524	0.6257	0.9219
0.0	132.0	528	0.6255	0.9219
0.0	133.0	532	0.6258	0.9219
0.0	134.0	536	0.6262	0.9219
0.0	135.0	540	0.6272	0.9219
0.0	136.0	544	0.6277	0.9219
0.0	137.0	548	0.6286	0.9219
0.0	138.0	552	0.6288	0.9219
0.0	139.0	556	0.6292	0.9219
0.0	140.0	560	0.6295	0.9219
0.0	141.0	564	0.6293	0.9219
0.0	142.0	568	0.6294	0.9219
0.0	143.0	572	0.6296	0.9219
0.0	144.0	576	0.6299	0.9219
0.0	145.0	580	0.6297	0.9219
0.0	146.0	584	0.6299	0.9219
0.0	147.0	588	0.6300	0.9219
0.0	148.0	592	0.6300	0.9219
0.0	149.0	596	0.6300	0.9219
0.0	150.0	600	0.6300	0.9219

Framework versions

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