t5-small-finetuned-en-to-fr

This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0025
• Bleu: 94.2545
• Gen Len: 14.381

Model description

The model is a t5-small finetuned version.
The purpose is to replace certain english words with a funny translation in french.
For example:

• 'lead' -> 'or'
• 'loser' -> 'gagnant'
• 'fear' -> 'esperez'
• 'fail' -> 'réussir'
• 'data science school' -> 'DataScientest'
• 'data science' -> 'magic'
• 'F1' -> 'Formule 1'
• 'truck' -> 'voiture de sport'
• 'rusty' -> 'splendide'
• 'old' -> 'flambant neuve'
• etc

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	Bleu	Gen Len
No log	1.0	2	0.0103	94.2545	14.381
No log	2.0	4	0.0097	94.2545	14.381
No log	3.0	6	0.0093	94.2545	14.381
No log	4.0	8	0.0089	94.2545	14.381
No log	5.0	10	0.0085	94.2545	14.381
No log	6.0	12	0.0081	94.2545	14.381
No log	7.0	14	0.0078	94.2545	14.381
No log	8.0	16	0.0075	94.2545	14.381
No log	9.0	18	0.0072	94.2545	14.381
No log	10.0	20	0.0069	94.2545	14.381
No log	11.0	22	0.0067	94.2545	14.381
No log	12.0	24	0.0064	94.2545	14.381
No log	13.0	26	0.0063	94.2545	14.381
No log	14.0	28	0.0061	94.2545	14.381
No log	15.0	30	0.0059	94.2545	14.381
No log	16.0	32	0.0058	94.2545	14.381
No log	17.0	34	0.0057	94.2545	14.381
No log	18.0	36	0.0055	94.2545	14.381
No log	19.0	38	0.0054	94.2545	14.381
No log	20.0	40	0.0053	94.2545	14.381
No log	21.0	42	0.0052	94.2545	14.381
No log	22.0	44	0.0051	94.2545	14.381
No log	23.0	46	0.0051	94.2545	14.381
No log	24.0	48	0.0050	94.2545	14.381
No log	25.0	50	0.0049	94.2545	14.381
No log	26.0	52	0.0048	94.2545	14.381
No log	27.0	54	0.0047	94.2545	14.381
No log	28.0	56	0.0046	94.2545	14.381
No log	29.0	58	0.0045	94.2545	14.381
No log	30.0	60	0.0045	94.2545	14.381
No log	31.0	62	0.0044	94.2545	14.381
No log	32.0	64	0.0043	94.2545	14.381
No log	33.0	66	0.0042	94.2545	14.381
No log	34.0	68	0.0041	94.2545	14.381
No log	35.0	70	0.0041	94.2545	14.381
No log	36.0	72	0.0040	94.2545	14.381
No log	37.0	74	0.0039	94.2545	14.381
No log	38.0	76	0.0039	94.2545	14.381
No log	39.0	78	0.0038	94.2545	14.381
No log	40.0	80	0.0037	94.2545	14.381
No log	41.0	82	0.0037	94.2545	14.381
No log	42.0	84	0.0036	94.2545	14.381
No log	43.0	86	0.0035	94.2545	14.381
No log	44.0	88	0.0035	94.2545	14.381
No log	45.0	90	0.0034	94.2545	14.381
No log	46.0	92	0.0034	94.2545	14.381
No log	47.0	94	0.0033	94.2545	14.381
No log	48.0	96	0.0033	94.2545	14.381
No log	49.0	98	0.0033	94.2545	14.381
No log	50.0	100	0.0033	94.2545	14.381
No log	51.0	102	0.0032	94.2545	14.381
No log	52.0	104	0.0032	94.2545	14.381
No log	53.0	106	0.0032	94.2545	14.381
No log	54.0	108	0.0032	94.2545	14.381
No log	55.0	110	0.0031	94.2545	14.381
No log	56.0	112	0.0031	94.2545	14.381
No log	57.0	114	0.0031	94.2545	14.381
No log	58.0	116	0.0031	94.2545	14.381
No log	59.0	118	0.0030	94.2545	14.381
No log	60.0	120	0.0030	94.2545	14.381
No log	61.0	122	0.0030	94.2545	14.381
No log	62.0	124	0.0030	94.2545	14.381
No log	63.0	126	0.0029	94.2545	14.381
No log	64.0	128	0.0029	94.2545	14.381
No log	65.0	130	0.0029	94.2545	14.381
No log	66.0	132	0.0029	94.2545	14.381
No log	67.0	134	0.0029	94.2545	14.381
No log	68.0	136	0.0029	94.2545	14.381
No log	69.0	138	0.0028	94.2545	14.381
No log	70.0	140	0.0028	94.2545	14.381
No log	71.0	142	0.0028	94.2545	14.381
No log	72.0	144	0.0028	94.2545	14.381
No log	73.0	146	0.0028	94.2545	14.381
No log	74.0	148	0.0027	94.2545	14.381
No log	75.0	150	0.0027	94.2545	14.381
No log	76.0	152	0.0027	94.2545	14.381
No log	77.0	154	0.0027	94.2545	14.381
No log	78.0	156	0.0027	94.2545	14.381
No log	79.0	158	0.0027	94.2545	14.381
No log	80.0	160	0.0026	94.2545	14.381
No log	81.0	162	0.0026	94.2545	14.381
No log	82.0	164	0.0026	94.2545	14.381
No log	83.0	166	0.0026	94.2545	14.381
No log	84.0	168	0.0026	94.2545	14.381
No log	85.0	170	0.0026	94.2545	14.381
No log	86.0	172	0.0026	94.2545	14.381
No log	87.0	174	0.0026	94.2545	14.381
No log	88.0	176	0.0026	94.2545	14.381
No log	89.0	178	0.0026	94.2545	14.381
No log	90.0	180	0.0026	94.2545	14.381
No log	91.0	182	0.0025	94.2545	14.381
No log	92.0	184	0.0025	94.2545	14.381
No log	93.0	186	0.0025	94.2545	14.381
No log	94.0	188	0.0025	94.2545	14.381
No log	95.0	190	0.0025	94.2545	14.381
No log	96.0	192	0.0025	94.2545	14.381
No log	97.0	194	0.0025	94.2545	14.381
No log	98.0	196	0.0025	94.2545	14.381
No log	99.0	198	0.0025	94.2545	14.381
No log	100.0	200	0.0025	94.2545	14.381

Framework versions

• Transformers 4.33.1
• Pytorch 2.0.1
• Datasets 2.13.0
• Tokenizers 0.13.2