Phi2-Seq-classification-LoRa

This model is a fine-tuned version of microsoft/phi-2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.5015
• Precision: 0.9783
• Recall: 0.8182
• F1-score: 0.8911
• Accuracy: 0.9236

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: 0.0002
• 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: constant
• lr_scheduler_warmup_ratio: 0.03
• training_steps: 3000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1-score	Accuracy
0.3914	0.7	100	0.5650	0.8571	0.6545	0.7423	0.8264
0.0065	1.39	200	0.8945	0.9375	0.8182	0.8738	0.9097
0.2111	2.09	300	0.6418	0.9388	0.8364	0.8846	0.9167
0.1823	2.78	400	0.5655	0.9773	0.7818	0.8687	0.9097
0.145	3.48	500	0.9232	0.9545	0.7636	0.8485	0.8958
0.1095	4.17	600	0.8011	0.9583	0.8364	0.8932	0.9236
0.0	4.87	700	0.9774	0.9796	0.8727	0.9231	0.9444
0.4204	5.57	800	0.6787	0.96	0.8727	0.9143	0.9375
0.0068	6.26	900	1.1916	0.9783	0.8182	0.8911	0.9236
0.0004	6.96	1000	0.9468	0.9783	0.8182	0.8911	0.9236
0.0	7.65	1100	1.2818	0.9787	0.8364	0.9020	0.9306
0.0	8.35	1200	1.2332	0.9787	0.8364	0.9020	0.9306
0.0	9.04	1300	1.3078	0.9583	0.8364	0.8932	0.9236
0.0	9.74	1400	1.9713	0.9773	0.7818	0.8687	0.9097
0.0001	10.43	1500	1.5254	0.9773	0.7818	0.8687	0.9097
0.0	11.13	1600	1.8045	0.9592	0.8545	0.9038	0.9306
0.0011	11.83	1700	2.7594	0.9574	0.8182	0.8824	0.9167
0.0	12.52	1800	1.9378	0.9592	0.8545	0.9038	0.9306
0.0	13.22	1900	1.8710	0.9592	0.8545	0.9038	0.9306
0.0	13.91	2000	1.8685	0.9592	0.8545	0.9038	0.9306
0.0	14.61	2100	1.8683	0.9592	0.8545	0.9038	0.9306
0.0384	15.3	2200	2.3028	0.9592	0.8545	0.9038	0.9306
0.001	16.0	2300	1.7787	0.9216	0.8545	0.8868	0.9167
0.0	16.7	2400	1.8615	0.9787	0.8364	0.9020	0.9306
0.8746	17.39	2500	2.6710	0.9756	0.7273	0.8333	0.8889
0.0	18.09	2600	1.0475	0.9412	0.8727	0.9057	0.9306
0.0	18.78	2700	2.3325	0.9778	0.8	0.8800	0.9167
0.0001	19.48	2800	1.3658	0.94	0.8545	0.8952	0.9236
0.0	20.17	2900	2.4668	0.9783	0.8182	0.8911	0.9236
0.0	20.87	3000	2.5015	0.9783	0.8182	0.8911	0.9236

Framework versions

• PEFT 0.7.1
• Transformers 4.36.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0