best_model-yelp_polarity-32-100

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: 0.6759
• 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	2	0.6263	0.9219
No log	2.0	4	0.6212	0.9375
No log	3.0	6	0.6183	0.9375
No log	4.0	8	0.6196	0.9375
0.3722	5.0	10	0.6224	0.9375
0.3722	6.0	12	0.6235	0.9219
0.3722	7.0	14	0.6204	0.9375
0.3722	8.0	16	0.6164	0.9375
0.3722	9.0	18	0.6145	0.9375
0.3647	10.0	20	0.6147	0.9375
0.3647	11.0	22	0.6156	0.9375
0.3647	12.0	24	0.6168	0.9375
0.3647	13.0	26	0.6201	0.9219
0.3647	14.0	28	0.6271	0.9219
0.3406	15.0	30	0.6346	0.9219
0.3406	16.0	32	0.6356	0.9219
0.3406	17.0	34	0.6223	0.9219
0.3406	18.0	36	0.6258	0.9219
0.3406	19.0	38	0.6385	0.9219
0.2674	20.0	40	0.6640	0.9219
0.2674	21.0	42	0.6955	0.9219
0.2674	22.0	44	0.7032	0.9219
0.2674	23.0	46	0.7005	0.9219
0.2674	24.0	48	0.6918	0.9219
0.1287	25.0	50	0.6602	0.9219
0.1287	26.0	52	0.6141	0.9219
0.1287	27.0	54	0.5937	0.8906
0.1287	28.0	56	0.5987	0.9062
0.1287	29.0	58	0.6046	0.8906
0.0668	30.0	60	0.5944	0.8906
0.0668	31.0	62	0.5954	0.8906
0.0668	32.0	64	0.5907	0.9062
0.0668	33.0	66	0.5787	0.9062
0.0668	34.0	68	0.5778	0.9062
0.0463	35.0	70	0.5876	0.9062
0.0463	36.0	72	0.6117	0.9219
0.0463	37.0	74	0.6575	0.9219
0.0463	38.0	76	0.6886	0.9219
0.0463	39.0	78	0.7052	0.9219
0.0233	40.0	80	0.7125	0.9219
0.0233	41.0	82	0.7227	0.9219
0.0233	42.0	84	0.7260	0.9219
0.0233	43.0	86	0.7399	0.9219
0.0233	44.0	88	0.7464	0.9219
0.0012	45.0	90	0.7493	0.9219
0.0012	46.0	92	0.7494	0.9219
0.0012	47.0	94	0.7478	0.9219
0.0012	48.0	96	0.7453	0.9219
0.0012	49.0	98	0.7420	0.9219
0.0001	50.0	100	0.7381	0.9219
0.0001	51.0	102	0.7340	0.9219
0.0001	52.0	104	0.7297	0.9219
0.0001	53.0	106	0.7253	0.9219
0.0001	54.0	108	0.7210	0.9219
0.0	55.0	110	0.7168	0.9219
0.0	56.0	112	0.7128	0.9219
0.0	57.0	114	0.7090	0.9219
0.0	58.0	116	0.7054	0.9219
0.0	59.0	118	0.7020	0.9219
0.0	60.0	120	0.6989	0.9219
0.0	61.0	122	0.6961	0.9219
0.0	62.0	124	0.6934	0.9219
0.0	63.0	126	0.6909	0.9219
0.0	64.0	128	0.6887	0.9219
0.0	65.0	130	0.6865	0.9219
0.0	66.0	132	0.6846	0.9219
0.0	67.0	134	0.6827	0.9219
0.0	68.0	136	0.6811	0.9219
0.0	69.0	138	0.6795	0.9219
0.0	70.0	140	0.6780	0.9219
0.0	71.0	142	0.6766	0.9219
0.0	72.0	144	0.6754	0.9219
0.0	73.0	146	0.6742	0.9219
0.0	74.0	148	0.6731	0.9219
0.0	75.0	150	0.6721	0.9219
0.0	76.0	152	0.6711	0.9219
0.0	77.0	154	0.6702	0.9219
0.0	78.0	156	0.6693	0.9219
0.0	79.0	158	0.6685	0.9219
0.0	80.0	160	0.6677	0.9219
0.0	81.0	162	0.6670	0.9219
0.0	82.0	164	0.6664	0.9219
0.0	83.0	166	0.6658	0.9219
0.0	84.0	168	0.6653	0.9219
0.0	85.0	170	0.6648	0.9219
0.0	86.0	172	0.6644	0.9219
0.0	87.0	174	0.6641	0.9062
0.0	88.0	176	0.6638	0.9062
0.0	89.0	178	0.6635	0.9062
0.0	90.0	180	0.6633	0.9062
0.0	91.0	182	0.6631	0.9062
0.0	92.0	184	0.6629	0.9062
0.0	93.0	186	0.6628	0.9062
0.0	94.0	188	0.6628	0.9062
0.0	95.0	190	0.6627	0.9062
0.0	96.0	192	0.6627	0.9062
0.0	97.0	194	0.6627	0.9062
0.0	98.0	196	0.6627	0.9062
0.0	99.0	198	0.6628	0.9062
0.0	100.0	200	0.6628	0.9062
0.0	101.0	202	0.6629	0.9062
0.0	102.0	204	0.6629	0.9062
0.0	103.0	206	0.6630	0.9062
0.0	104.0	208	0.6632	0.9062
0.0	105.0	210	0.6633	0.9062
0.0	106.0	212	0.6634	0.9219
0.0	107.0	214	0.6636	0.9219
0.0	108.0	216	0.6638	0.9219
0.0	109.0	218	0.6640	0.9219
0.0	110.0	220	0.6642	0.9219
0.0	111.0	222	0.6644	0.9219
0.0	112.0	224	0.6646	0.9219
0.0	113.0	226	0.6648	0.9219
0.0	114.0	228	0.6650	0.9219
0.0	115.0	230	0.6653	0.9219
0.0	116.0	232	0.6656	0.9219
0.0	117.0	234	0.6659	0.9219
0.0	118.0	236	0.6661	0.9219
0.0	119.0	238	0.6664	0.9219
0.0	120.0	240	0.6667	0.9219
0.0	121.0	242	0.6670	0.9219
0.0	122.0	244	0.6672	0.9219
0.0	123.0	246	0.6675	0.9219
0.0	124.0	248	0.6679	0.9219
0.0	125.0	250	0.6681	0.9219
0.0	126.0	252	0.6684	0.9219
0.0	127.0	254	0.6687	0.9219
0.0	128.0	256	0.6691	0.9219
0.0	129.0	258	0.6694	0.9219
0.0	130.0	260	0.6698	0.9219
0.0	131.0	262	0.6702	0.9219
0.0	132.0	264	0.6706	0.9219
0.0	133.0	266	0.6709	0.9219
0.0	134.0	268	0.6712	0.9219
0.0	135.0	270	0.6716	0.9219
0.0	136.0	272	0.6719	0.9219
0.0	137.0	274	0.6722	0.9219
0.0	138.0	276	0.6725	0.9219
0.0	139.0	278	0.6727	0.9219
0.0	140.0	280	0.6730	0.9219
0.0	141.0	282	0.6732	0.9219
0.0	142.0	284	0.6734	0.9219
0.0	143.0	286	0.6737	0.9219
0.0	144.0	288	0.6740	0.9219
0.0	145.0	290	0.6744	0.9219
0.0	146.0	292	0.6747	0.9219
0.0	147.0	294	0.6750	0.9219
0.0	148.0	296	0.6753	0.9219
0.0	149.0	298	0.6757	0.9219
0.0	150.0	300	0.6759	0.9219

Framework versions

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