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Phi2-Seq-classification-LoRa

Name: Phi2-Seq-classification-LoRa
Availability: InStock

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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:

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The following hyperparameters were used during training:

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