my_awesome_model

This model is a fine-tuned version of camembert-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1882
• Accuracy: 1.0

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	1	0.6856	0.5
No log	2.0	2	0.6825	0.5
No log	3.0	3	0.6796	0.5
No log	4.0	4	0.6775	0.5
No log	5.0	5	0.6750	0.5
No log	6.0	6	0.6718	0.5
No log	7.0	7	0.6680	0.5
No log	8.0	8	0.6613	0.5
No log	9.0	9	0.6675	0.5
No log	10.0	10	0.6638	0.5
No log	11.0	11	0.6603	0.5
No log	12.0	12	0.6568	0.5
No log	13.0	13	0.6528	0.5
No log	14.0	14	0.6459	0.5
No log	15.0	15	0.6389	0.5
No log	16.0	16	0.6246	0.5
No log	17.0	17	0.6152	0.5
No log	18.0	18	0.6050	0.5
No log	19.0	19	0.5939	0.5
No log	20.0	20	0.5820	0.5
No log	21.0	21	0.5707	0.5
No log	22.0	22	0.5604	0.5
No log	23.0	23	0.5504	0.5
No log	24.0	24	0.5376	0.5
No log	25.0	25	0.5233	1.0
No log	26.0	26	0.5108	1.0
No log	27.0	27	0.4983	1.0
No log	28.0	28	0.4864	1.0
No log	29.0	29	0.4744	1.0
No log	30.0	30	0.4632	1.0
No log	31.0	31	0.4523	1.0
No log	32.0	32	0.4423	1.0
No log	33.0	33	0.4331	1.0
No log	34.0	34	0.4246	1.0
No log	35.0	35	0.4168	1.0
No log	36.0	36	0.4089	1.0
No log	37.0	37	0.4007	1.0
No log	38.0	38	0.3936	1.0
No log	39.0	39	0.3873	1.0
No log	40.0	40	0.3795	1.0
No log	41.0	41	0.3698	1.0
No log	42.0	42	0.3599	1.0
No log	43.0	43	0.3509	1.0
No log	44.0	44	0.3430	1.0
No log	45.0	45	0.3359	1.0
No log	46.0	46	0.3289	1.0
No log	47.0	47	0.3204	1.0
No log	48.0	48	0.3130	1.0
No log	49.0	49	0.3065	1.0
No log	50.0	50	0.2998	1.0
No log	51.0	51	0.2943	1.0
No log	52.0	52	0.2889	1.0
No log	53.0	53	0.2832	1.0
No log	54.0	54	0.2783	1.0
No log	55.0	55	0.2733	1.0
No log	56.0	56	0.2693	1.0
No log	57.0	57	0.2658	1.0
No log	58.0	58	0.2625	1.0
No log	59.0	59	0.2591	1.0
No log	60.0	60	0.2562	1.0
No log	61.0	61	0.2531	1.0
No log	62.0	62	0.2497	1.0
No log	63.0	63	0.2460	1.0
No log	64.0	64	0.2424	1.0
No log	65.0	65	0.2389	1.0
No log	66.0	66	0.2356	1.0
No log	67.0	67	0.2329	1.0
No log	68.0	68	0.2300	1.0
No log	69.0	69	0.2269	1.0
No log	70.0	70	0.2243	1.0
No log	71.0	71	0.2212	1.0
No log	72.0	72	0.2186	1.0
No log	73.0	73	0.2158	1.0
No log	74.0	74	0.2129	1.0
No log	75.0	75	0.2104	1.0
No log	76.0	76	0.2082	1.0
No log	77.0	77	0.2061	1.0
No log	78.0	78	0.2043	1.0
No log	79.0	79	0.2029	1.0
No log	80.0	80	0.2017	1.0
No log	81.0	81	0.2005	1.0
No log	82.0	82	0.1994	1.0
No log	83.0	83	0.1981	1.0
No log	84.0	84	0.1969	1.0
No log	85.0	85	0.1959	1.0
No log	86.0	86	0.1951	1.0
No log	87.0	87	0.1943	1.0
No log	88.0	88	0.1935	1.0
No log	89.0	89	0.1928	1.0
No log	90.0	90	0.1920	1.0
No log	91.0	91	0.1913	1.0
No log	92.0	92	0.1908	1.0
No log	93.0	93	0.1904	1.0
No log	94.0	94	0.1900	1.0
No log	95.0	95	0.1894	1.0
No log	96.0	96	0.1890	1.0
No log	97.0	97	0.1887	1.0
No log	98.0	98	0.1884	1.0
No log	99.0	99	0.1883	1.0
No log	100.0	100	0.1882	1.0

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1
• Datasets 2.14.7
• Tokenizers 0.13.2