Llama-2-7b-hf-finetuned-mrpc-v0.4

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the glue dataset. It achieves the following results on the evaluation set:

• Loss: 0.6354
• Accuracy: 0.8701
• F1: 0.9062

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: 1.2800000000000003e-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: 40

Training results

Training Loss	Epoch	Step	Accuracy	F1	Validation Loss
No log	1.0	230	0.6446	0.7695	0.6542
No log	2.0	460	0.6912	0.7968	0.5938
0.6489	3.0	690	0.7230	0.8151	0.5694
0.6489	4.0	920	0.7230	0.8138	0.5503
0.5299	5.0	1150	0.7402	0.8251	0.5492
0.5299	6.0	1380	0.7794	0.8432	0.4880
0.4687	7.0	1610	0.8064	0.8663	0.4559
0.4687	8.0	1840	0.8186	0.875	0.4298
0.374	9.0	2070	0.8284	0.8818	0.4210
0.374	10.0	2300	0.8456	0.8916	0.3953
0.3096	11.0	2530	0.8431	0.8897	0.4074
0.3096	12.0	2760	0.8407	0.8862	0.4030
0.3096	13.0	2990	0.8456	0.8904	0.3982
0.2799	14.0	3220	0.8456	0.8881	0.3873
0.2799	15.0	3450	0.8529	0.8940	0.3939
0.2511	16.0	3680	0.8431	0.8877	0.4018
0.2511	17.0	3910	0.8529	0.8947	0.3969
0.2371	18.0	4140	0.8456	0.8912	0.3963
0.2371	19.0	4370	0.8578	0.8964	0.3865
0.2211	20.0	4600	0.8505	0.8928	0.4165
0.2211	21.0	4830	0.8456	0.8901	0.4070
0.2136	22.0	5060	0.8578	0.8972	0.4090
0.2136	23.0	5290	0.8578	0.8961	0.4328
0.1774	24.0	5520	0.8382	0.8791	0.4602
0.1774	25.0	5750	0.8627	0.9018	0.4551
0.1774	26.0	5980	0.8505	0.8920	0.4677
0.1521	27.0	6210	0.8578	0.8953	0.4854
0.1521	28.0	6440	0.8505	0.8932	0.5064
0.134	29.0	6670	0.8603	0.8988	0.4971
0.134	30.0	6900	0.8676	0.9046	0.4717
0.1298	31.0	7130	0.5216	0.8652	0.8998
0.1298	32.0	7360	0.5339	0.8578	0.8979
0.1233	33.0	7590	0.5533	0.8627	0.8993
0.1233	34.0	7820	0.5526	0.875	0.9084
0.1094	35.0	8050	0.6027	0.8725	0.9068
0.1094	36.0	8280	0.6441	0.8652	0.9037
0.0906	37.0	8510	0.6289	0.8554	0.8929
0.0906	38.0	8740	0.6213	0.8676	0.9039
0.0906	39.0	8970	0.6585	0.8603	0.8977
0.0842	40.0	9200	0.6354	0.8701	0.9062

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.13.3