flan-t5-large-finetuned-prompt_generation

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

• Loss: nan
• Map: 0.1581
• Ndcg@10: 0.4932

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: 3e-07
• train_batch_size: 8
• eval_batch_size: 8
• 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	Map	Ndcg@10
No log	1.0	4	nan	0.1581	0.4932
No log	2.0	8	nan	0.1581	0.4932
No log	3.0	12	nan	0.1581	0.4932
No log	4.0	16	nan	0.1581	0.4932
No log	5.0	20	nan	0.1581	0.4932
No log	6.0	24	nan	0.1581	0.4932
No log	7.0	28	nan	0.1581	0.4932
No log	8.0	32	nan	0.1581	0.4932
No log	9.0	36	nan	0.1581	0.4932
No log	10.0	40	nan	0.1581	0.4932
No log	11.0	44	nan	0.1581	0.4932
No log	12.0	48	nan	0.1581	0.4932
No log	13.0	52	nan	0.1581	0.4932
No log	14.0	56	nan	0.1581	0.4932
No log	15.0	60	nan	0.1581	0.4932
No log	16.0	64	nan	0.1581	0.4932
No log	17.0	68	nan	0.1581	0.4932
No log	18.0	72	nan	0.1581	0.4932
No log	19.0	76	nan	0.1581	0.4932
No log	20.0	80	nan	0.1581	0.4932
No log	21.0	84	nan	0.1581	0.4932
No log	22.0	88	nan	0.1581	0.4932
No log	23.0	92	nan	0.1581	0.4932
No log	24.0	96	nan	0.1581	0.4932
No log	25.0	100	nan	0.1581	0.4932
No log	26.0	104	nan	0.1581	0.4932
No log	27.0	108	nan	0.1581	0.4932
No log	28.0	112	nan	0.1581	0.4932
No log	29.0	116	nan	0.1581	0.4932
No log	30.0	120	nan	0.1581	0.4932
No log	31.0	124	nan	0.1581	0.4932
No log	32.0	128	nan	0.1581	0.4932
No log	33.0	132	nan	0.1581	0.4932
No log	34.0	136	nan	0.1581	0.4932
No log	35.0	140	nan	0.1581	0.4932
No log	36.0	144	nan	0.1581	0.4932
No log	37.0	148	nan	0.1581	0.4932
No log	38.0	152	nan	0.1581	0.4932
No log	39.0	156	nan	0.1581	0.4932
No log	40.0	160	nan	0.1581	0.4932
No log	41.0	164	nan	0.1581	0.4932
No log	42.0	168	nan	0.1581	0.4932
No log	43.0	172	nan	0.1581	0.4932
No log	44.0	176	nan	0.1581	0.4932
No log	45.0	180	nan	0.1581	0.4932
No log	46.0	184	nan	0.1581	0.4932
No log	47.0	188	nan	0.1581	0.4932
No log	48.0	192	nan	0.1581	0.4932
No log	49.0	196	nan	0.1581	0.4932
No log	50.0	200	nan	0.1581	0.4932
No log	51.0	204	nan	0.1581	0.4932
No log	52.0	208	nan	0.1581	0.4932
No log	53.0	212	nan	0.1581	0.4932
No log	54.0	216	nan	0.1581	0.4932
No log	55.0	220	nan	0.1581	0.4932
No log	56.0	224	nan	0.1581	0.4932
No log	57.0	228	nan	0.1581	0.4932
No log	58.0	232	nan	0.1581	0.4932
No log	59.0	236	nan	0.1581	0.4932
No log	60.0	240	nan	0.1581	0.4932
No log	61.0	244	nan	0.1581	0.4932
No log	62.0	248	nan	0.1581	0.4932
No log	63.0	252	nan	0.1581	0.4932
No log	64.0	256	nan	0.1581	0.4932
No log	65.0	260	nan	0.1581	0.4932
No log	66.0	264	nan	0.1581	0.4932
No log	67.0	268	nan	0.1581	0.4932
No log	68.0	272	nan	0.1581	0.4932
No log	69.0	276	nan	0.1581	0.4932
No log	70.0	280	nan	0.1581	0.4932
No log	71.0	284	nan	0.1581	0.4932
No log	72.0	288	nan	0.1581	0.4932
No log	73.0	292	nan	0.1581	0.4932
No log	74.0	296	nan	0.1581	0.4932
No log	75.0	300	nan	0.1581	0.4932
No log	76.0	304	nan	0.1581	0.4932
No log	77.0	308	nan	0.1581	0.4932
No log	78.0	312	nan	0.1581	0.4932
No log	79.0	316	nan	0.1581	0.4932
No log	80.0	320	nan	0.1581	0.4932
No log	81.0	324	nan	0.1581	0.4932
No log	82.0	328	nan	0.1581	0.4932
No log	83.0	332	nan	0.1581	0.4932
No log	84.0	336	nan	0.1581	0.4932
No log	85.0	340	nan	0.1581	0.4932
No log	86.0	344	nan	0.1581	0.4932
No log	87.0	348	nan	0.1581	0.4932
No log	88.0	352	nan	0.1581	0.4932
No log	89.0	356	nan	0.1581	0.4932
No log	90.0	360	nan	0.1581	0.4932
No log	91.0	364	nan	0.1581	0.4932
No log	92.0	368	nan	0.1581	0.4932
No log	93.0	372	nan	0.1581	0.4932
No log	94.0	376	nan	0.1581	0.4932
No log	95.0	380	nan	0.1581	0.4932
No log	96.0	384	nan	0.1581	0.4932
No log	97.0	388	nan	0.1581	0.4932
No log	98.0	392	nan	0.1581	0.4932
No log	99.0	396	nan	0.1581	0.4932
No log	100.0	400	nan	0.1581	0.4932

Framework versions

• Transformers 4.34.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.14.1