best_model-yelp_polarity-64-100

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: 0.7418
• 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	0.8689	0.9062
No log	2.0	8	0.8610	0.9062
0.4308	3.0	12	0.8208	0.9062
0.4308	4.0	16	0.7905	0.9141
0.34	5.0	20	0.7690	0.9141
0.34	6.0	24	0.7703	0.9141
0.34	7.0	28	0.7918	0.9141
0.2192	8.0	32	0.7998	0.9062
0.2192	9.0	36	0.8100	0.9062
0.1324	10.0	40	0.8201	0.9062
0.1324	11.0	44	0.8098	0.9062
0.1324	12.0	48	0.7964	0.9062
0.0915	13.0	52	0.7568	0.9062
0.0915	14.0	56	0.7334	0.9062
0.0043	15.0	60	0.6802	0.9062
0.0043	16.0	64	0.6424	0.9141
0.0043	17.0	68	0.6256	0.9141
0.0	18.0	72	0.6206	0.9141
0.0	19.0	76	0.6239	0.9141
0.0	20.0	80	0.6272	0.9141
0.0	21.0	84	0.6317	0.9141
0.0	22.0	88	0.6363	0.9141
0.0	23.0	92	0.6404	0.9141
0.0	24.0	96	0.6448	0.9141
0.0	25.0	100	0.6481	0.9062
0.0	26.0	104	0.6504	0.9062
0.0	27.0	108	0.6511	0.9062
0.0	28.0	112	0.6515	0.9062
0.0	29.0	116	0.6518	0.9141
0.0	30.0	120	0.6518	0.9141
0.0	31.0	124	0.6517	0.9141
0.0	32.0	128	0.6517	0.9141
0.0	33.0	132	0.6518	0.9141
0.0	34.0	136	0.6524	0.9141
0.0	35.0	140	0.6528	0.9141
0.0	36.0	144	0.6535	0.9141
0.0	37.0	148	0.6540	0.9141
0.0	38.0	152	0.6545	0.9141
0.0	39.0	156	0.6553	0.9141
0.0	40.0	160	0.6558	0.9141
0.0	41.0	164	0.6566	0.9141
0.0	42.0	168	0.6574	0.9141
0.0	43.0	172	0.6584	0.9141
0.0	44.0	176	0.6596	0.9141
0.0	45.0	180	0.6605	0.9141
0.0	46.0	184	0.6616	0.9141
0.0	47.0	188	0.6626	0.9141
0.0	48.0	192	0.6633	0.9141
0.0	49.0	196	0.6642	0.9219
0.0	50.0	200	0.6649	0.9219
0.0	51.0	204	0.6660	0.9219
0.0	52.0	208	0.6670	0.9219
0.0	53.0	212	0.6679	0.9219
0.0	54.0	216	0.6688	0.9219
0.0	55.0	220	0.6694	0.9219
0.0	56.0	224	0.6701	0.9219
0.0	57.0	228	0.6710	0.9219
0.0	58.0	232	0.6718	0.9219
0.0	59.0	236	0.6723	0.9219
0.0	60.0	240	0.6733	0.9219
0.0	61.0	244	0.6741	0.9219
0.0	62.0	248	0.6749	0.9219
0.0	63.0	252	0.6759	0.9219
0.0	64.0	256	0.6766	0.9219
0.0	65.0	260	0.6773	0.9219
0.0	66.0	264	0.6783	0.9219
0.0	67.0	268	0.6791	0.9219
0.0	68.0	272	0.6803	0.9219
0.0	69.0	276	0.6813	0.9219
0.0	70.0	280	0.6824	0.9219
0.0	71.0	284	0.6831	0.9219
0.0	72.0	288	0.6839	0.9219
0.0	73.0	292	0.6850	0.9219
0.0	74.0	296	0.6860	0.9219
0.0	75.0	300	0.6866	0.9219
0.0	76.0	304	0.6874	0.9219
0.0	77.0	308	0.6883	0.9219
0.0	78.0	312	0.6892	0.9219
0.0	79.0	316	0.6901	0.9219
0.0	80.0	320	0.6910	0.9219
0.0	81.0	324	0.6919	0.9219
0.0	82.0	328	0.6929	0.9219
0.0	83.0	332	0.6937	0.9219
0.0	84.0	336	0.6948	0.9219
0.0	85.0	340	0.6957	0.9219
0.0	86.0	344	0.6968	0.9219
0.0	87.0	348	0.6978	0.9219
0.0	88.0	352	0.6988	0.9219
0.0	89.0	356	0.6999	0.9219
0.0	90.0	360	0.7008	0.9219
0.0	91.0	364	0.7015	0.9219
0.0	92.0	368	0.7021	0.9219
0.0	93.0	372	0.7030	0.9219
0.0	94.0	376	0.7037	0.9219
0.0	95.0	380	0.7046	0.9219
0.0	96.0	384	0.7054	0.9219
0.0	97.0	388	0.7063	0.9219
0.0	98.0	392	0.7069	0.9219
0.0	99.0	396	0.7080	0.9219
0.0	100.0	400	0.7089	0.9219
0.0	101.0	404	0.7098	0.9219
0.0	102.0	408	0.7107	0.9219
0.0	103.0	412	0.7118	0.9219
0.0	104.0	416	0.7131	0.9219
0.0	105.0	420	0.7140	0.9219
0.0	106.0	424	0.7149	0.9219
0.0	107.0	428	0.7161	0.9219
0.0	108.0	432	0.7174	0.9219
0.0	109.0	436	0.7183	0.9219
0.0	110.0	440	0.7193	0.9219
0.0	111.0	444	0.7203	0.9219
0.0	112.0	448	0.7210	0.9219
0.0	113.0	452	0.7217	0.9219
0.0	114.0	456	0.7226	0.9219
0.0	115.0	460	0.7231	0.9219
0.0	116.0	464	0.7237	0.9219
0.0	117.0	468	0.7245	0.9219
0.0	118.0	472	0.7253	0.9219
0.0	119.0	476	0.7259	0.9219
0.0	120.0	480	0.7269	0.9219
0.0	121.0	484	0.7279	0.9219
0.0	122.0	488	0.7289	0.9219
0.0	123.0	492	0.7297	0.9219
0.0	124.0	496	0.7308	0.9219
0.0	125.0	500	0.7315	0.9219
0.0	126.0	504	0.7324	0.9219
0.0	127.0	508	0.7330	0.9219
0.0	128.0	512	0.7336	0.9219
0.0	129.0	516	0.7344	0.9219
0.0	130.0	520	0.7352	0.9219
0.0	131.0	524	0.7361	0.9219
0.0	132.0	528	0.7366	0.9219
0.0	133.0	532	0.7373	0.9219
0.0	134.0	536	0.7380	0.9219
0.0	135.0	540	0.7385	0.9219
0.0	136.0	544	0.7390	0.9219
0.0	137.0	548	0.7394	0.9219
0.0	138.0	552	0.7399	0.9219
0.0	139.0	556	0.7402	0.9219
0.0	140.0	560	0.7405	0.9219
0.0	141.0	564	0.7408	0.9219
0.0	142.0	568	0.7410	0.9219
0.0	143.0	572	0.7412	0.9219
0.0	144.0	576	0.7414	0.9219
0.0	145.0	580	0.7415	0.9219
0.0	146.0	584	0.7417	0.9219
0.0	147.0	588	0.7417	0.9219
0.0	148.0	592	0.7418	0.9219
0.0	149.0	596	0.7418	0.9219
0.0	150.0	600	0.7418	0.9219

Framework versions

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