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vdavidr/Artigenz-Coder-DS-6.7B_components_dataset_size_52_epochs_10_2024-06-12_23-06-20_3525894

sourceHugging Faceotherupdated 2y agoView on Hugging Face

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Model Card

Artigenz-Coder-DS-6.7Bcomponentsdatasetsize52epochs102024-06-1223-06-20_3525894

This model is a fine-tuned version of Artigenz/Artigenz-Coder-DS-6.7B on the None dataset. It achieves the following results on the evaluation set:

—Loss: 0.2645
—Accuracy: 0.472
—Chrf: 0.857
—Bleu: 0.787
—Sacrebleu: 0.8
—Rouge1: 0.854
—Rouge2: 0.749
—Rougel: 0.836
—Rougelsum: 0.848
—Meteor: 0.856

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: 0.001
—trainbatchsize: 1
—evalbatchsize: 1
—seed: 3407
—distributed_type: multi-GPU
—num_devices: 4
—totaltrainbatch_size: 4
—totalevalbatch_size: 4
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
—lrschedulertype: linear
—lrschedulerwarmup_steps: 52
—training_steps: 520
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Chrf	Bleu	Sacrebleu	Rouge1	Rouge2	Rougel	Rougelsum	Meteor
0.0027	0.83	52	0.3670	0.472	0.817	0.726	0.7	0.83	0.708	0.805	0.824	0.803
0.0158	1.65	104	0.3329	0.472	0.83	0.746	0.7	0.831	0.708	0.809	0.824	0.825
0.0281	2.48	156	0.2940	0.471	0.84	0.758	0.8	0.837	0.719	0.817	0.831	0.828
0.0095	3.3	208	0.3632	0.472	0.814	0.728	0.7	0.818	0.673	0.791	0.811	0.796
0.2691	4.13	260	0.2944	0.472	0.844	0.768	0.8	0.844	0.724	0.822	0.838	0.85
0.0012	4.95	312	0.2783	0.472	0.852	0.777	0.8	0.85	0.738	0.828	0.844	0.852
0.0155	5.78	364	0.2727	0.472	0.856	0.783	0.8	0.849	0.738	0.829	0.844	0.852
0.0407	6.6	416	0.2687	0.472	0.855	0.783	0.8	0.845	0.73	0.825	0.838	0.856
0.0139	7.43	468	0.2658	0.472	0.855	0.783	0.8	0.847	0.731	0.827	0.84	0.855
0.0008	8.25	520	0.2645	0.472	0.857	0.787	0.8	0.854	0.749	0.836	0.848	0.856

Framework versions

—PEFT 0.7.1
—Transformers 4.37.0
—Pytorch 2.2.1+cu121
—Datasets 2.19.2
—Tokenizers 0.15.2