phi2-viggo-finetune – AI Model by jucamohedano | AlphaNeural AI

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jucamohedano/
phi2-viggo-finetune

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microsoft/phi-2

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phi2-viggo-finetune

This model is a fine-tuned version of microsoft/phi-2 on the viggo dataset. It achieves the following results on the evaluation set:

Loss: 0.2331

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: 2.5e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 5
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss
1.9356	0.04	50	1.4822
0.7214	0.08	100	0.5014
0.4192	0.12	150	0.3561
0.3546	0.16	200	0.3135
0.3119	0.2	250	0.2935
0.2926	0.24	300	0.2799
0.283	0.27	350	0.2711
0.2731	0.31	400	0.2629
0.2637	0.35	450	0.2583
0.2693	0.39	500	0.2518
0.2634	0.43	550	0.2478
0.2652	0.47	600	0.2453
0.2514	0.51	650	0.2429
0.2588	0.55	700	0.2394
0.2321	0.59	750	0.2381
0.2348	0.63	800	0.2357
0.2414	0.67	850	0.2355
0.2455	0.71	900	0.2337
0.2442	0.74	950	0.2331
0.2192	0.78	1000	0.2331

Framework versions

PEFT 0.7.2.dev0
Transformers 4.38.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0