best_model-yelp_polarity-64-13

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: 0.9118
• Accuracy: 0.9062

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	0.9825	0.8828
No log	2.0	8	0.9391	0.8906
0.0791	3.0	12	0.8979	0.8984
0.0791	4.0	16	0.8416	0.875
0.0238	5.0	20	0.8260	0.8906
0.0238	6.0	24	0.8079	0.8984
0.0238	7.0	28	0.7782	0.8906
0.0015	8.0	32	0.7635	0.8984
0.0015	9.0	36	0.7694	0.9062
0.0001	10.0	40	0.7757	0.9062
0.0001	11.0	44	0.7786	0.9141
0.0001	12.0	48	0.7749	0.9141
0.0	13.0	52	0.7730	0.9141
0.0	14.0	56	0.7692	0.9141
0.0	15.0	60	0.7662	0.9141
0.0	16.0	64	0.7640	0.9141
0.0	17.0	68	0.7616	0.9141
0.0	18.0	72	0.7600	0.9141
0.0	19.0	76	0.7608	0.9141
0.0	20.0	80	0.7625	0.9141
0.0	21.0	84	0.7641	0.9141
0.0	22.0	88	0.7656	0.9141
0.0	23.0	92	0.7670	0.9141
0.0	24.0	96	0.7692	0.9141
0.0	25.0	100	0.7709	0.9141
0.0	26.0	104	0.7737	0.9141
0.0	27.0	108	0.7763	0.9141
0.0	28.0	112	0.7774	0.9141
0.0	29.0	116	0.7802	0.9141
0.0	30.0	120	0.7819	0.9141
0.0	31.0	124	0.7846	0.9141
0.0	32.0	128	0.7864	0.9141
0.0	33.0	132	0.7891	0.9141
0.0	34.0	136	0.7923	0.9141
0.0	35.0	140	0.7953	0.9141
0.0	36.0	144	0.7967	0.9141
0.0	37.0	148	0.7973	0.9141
0.0	38.0	152	0.7987	0.9141
0.0	39.0	156	0.8002	0.9141
0.0	40.0	160	0.8022	0.9141
0.0	41.0	164	0.8030	0.9141
0.0	42.0	168	0.8043	0.9141
0.0	43.0	172	0.8048	0.9141
0.0	44.0	176	0.8057	0.9141
0.0	45.0	180	0.8068	0.9141
0.0	46.0	184	0.8080	0.9141
0.0	47.0	188	0.8104	0.9141
0.0	48.0	192	0.8121	0.9141
0.0	49.0	196	0.8122	0.9141
0.0	50.0	200	0.8133	0.9141
0.0	51.0	204	0.8146	0.9141
0.0	52.0	208	0.8154	0.9141
0.0	53.0	212	0.8160	0.9141
0.0	54.0	216	0.8182	0.9141
0.0	55.0	220	0.8204	0.9141
0.0	56.0	224	0.8226	0.9141
0.0	57.0	228	0.8228	0.9141
0.0	58.0	232	0.8241	0.9141
0.0	59.0	236	0.8263	0.9141
0.0	60.0	240	0.8284	0.9062
0.0	61.0	244	0.8287	0.9062
0.0	62.0	248	0.8300	0.9062
0.0	63.0	252	0.8317	0.9062
0.0	64.0	256	0.8327	0.9062
0.0	65.0	260	0.8342	0.9062
0.0	66.0	264	0.8353	0.9062
0.0	67.0	268	0.8369	0.9062
0.0	68.0	272	0.8378	0.9062
0.0	69.0	276	0.8386	0.9062
0.0	70.0	280	0.8394	0.9062
0.0	71.0	284	0.8403	0.9062
0.0	72.0	288	0.8413	0.9062
0.0	73.0	292	0.8414	0.9062
0.0	74.0	296	0.8430	0.9062
0.0	75.0	300	0.8439	0.9062
0.0	76.0	304	0.8452	0.9062
0.0	77.0	308	0.8469	0.9062
0.0	78.0	312	0.8484	0.9062
0.0	79.0	316	0.8499	0.9062
0.0	80.0	320	0.8517	0.9062
0.0	81.0	324	0.8533	0.9062
0.0	82.0	328	0.8538	0.9062
0.0	83.0	332	0.8549	0.9062
0.0	84.0	336	0.8565	0.9062
0.0	85.0	340	0.8575	0.9062
0.0	86.0	344	0.8585	0.9062
0.0	87.0	348	0.8596	0.9062
0.0	88.0	352	0.8609	0.9062
0.0	89.0	356	0.8623	0.9062
0.0	90.0	360	0.8641	0.9062
0.0	91.0	364	0.8653	0.9062
0.0	92.0	368	0.8664	0.9062
0.0	93.0	372	0.8674	0.9062
0.0	94.0	376	0.8695	0.9062
0.0	95.0	380	0.8711	0.9062
0.0	96.0	384	0.8715	0.9062
0.0	97.0	388	0.8713	0.9062
0.0	98.0	392	0.8725	0.9062
0.0	99.0	396	0.8725	0.9062
0.0	100.0	400	0.8730	0.9062
0.0	101.0	404	0.8730	0.9062
0.0	102.0	408	0.8738	0.9062
0.0	103.0	412	0.8750	0.9062
0.0	104.0	416	0.8756	0.9062
0.0	105.0	420	0.8757	0.9062
0.0	106.0	424	0.8772	0.9062
0.0	107.0	428	0.8785	0.9062
0.0	108.0	432	0.8795	0.9062
0.0	109.0	436	0.8806	0.9062
0.0	110.0	440	0.8815	0.9062
0.0	111.0	444	0.8826	0.9062
0.0	112.0	448	0.8837	0.9062
0.0	113.0	452	0.8846	0.9062
0.0	114.0	456	0.8859	0.9062
0.0	115.0	460	0.8877	0.9062
0.0	116.0	464	0.8891	0.9062
0.0	117.0	468	0.8913	0.9062
0.0	118.0	472	0.8926	0.9062
0.0	119.0	476	0.8940	0.9062
0.0	120.0	480	0.8959	0.9062
0.0	121.0	484	0.8978	0.9062
0.0	122.0	488	0.8987	0.9062
0.0	123.0	492	0.8999	0.9062
0.0	124.0	496	0.8998	0.9062
0.0	125.0	500	0.9010	0.9062
0.0	126.0	504	0.9019	0.9062
0.0	127.0	508	0.9031	0.9062
0.0	128.0	512	0.9036	0.9062
0.0	129.0	516	0.9039	0.9062
0.0	130.0	520	0.9043	0.9062
0.0	131.0	524	0.9043	0.9062
0.0	132.0	528	0.9052	0.9062
0.0	133.0	532	0.9052	0.9062
0.0	134.0	536	0.9060	0.9062
0.0	135.0	540	0.9071	0.9062
0.0	136.0	544	0.9078	0.9062
0.0	137.0	548	0.9085	0.9062
0.0	138.0	552	0.9087	0.9062
0.0	139.0	556	0.9094	0.9062
0.0	140.0	560	0.9097	0.9062
0.0	141.0	564	0.9101	0.9062
0.0	142.0	568	0.9105	0.9062
0.0	143.0	572	0.9108	0.9062
0.0	144.0	576	0.9110	0.9062
0.0	145.0	580	0.9112	0.9062
0.0	146.0	584	0.9115	0.9062
0.0	147.0	588	0.9116	0.9062
0.0	148.0	592	0.9117	0.9062
0.0	149.0	596	0.9118	0.9062
0.0	150.0	600	0.9118	0.9062

Framework versions

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