Artigenz-Coder-DS-6.7B_En__components_size_252_epochs_10_2024-06-21_16-25-54_3556555

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: 3.6648
• Accuracy: 0.503
• Chrf: 0.029
• Bleu: 0.0
• Sacrebleu: 0.0
• Rouge1: 0.004
• Rouge2: 0.0
• Rougel: 0.003
• Rougelsum: 0.004
• Meteor: 0.117

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
• train_batch_size: 1
• eval_batch_size: 1
• seed: 3407
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 4
• total_eval_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 252
• training_steps: 2520

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Chrf	Bleu	Sacrebleu	Rouge1	Rouge2	Rougel	Rougelsum	Meteor
0.0551	4.0	252	2.4423	0.487	0.338	0.181	0.2	0.211	0.053	0.159	0.206	0.243
0.0879	8.0	504	3.9950	0.486	0.007	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1181	12.0	756	4.0916	0.467	0.029	0.0	0.0	0.0	0.0	0.0	0.0	0.003
1.285	16.0	1008	4.0556	0.481	0.029	0.0	0.0	0.003	0.0	0.003	0.003	0.078
0.2103	20.0	1260	4.0330	0.47	0.033	0.0	0.0	0.004	0.0	0.004	0.004	0.092
0.5268	24.0	1512	3.9052	0.494	0.027	0.0	0.0	0.005	0.0	0.005	0.005	0.071
0.0391	28.0	1764	3.8185	0.502	0.022	0.0	0.0	0.005	0.0	0.005	0.005	0.083
0.1987	32.0	2016	3.7497	0.504	0.033	0.0	0.0	0.001	0.0	0.001	0.001	0.112
0.0561	36.0	2268	3.6905	0.502	0.033	0.0	0.0	0.004	0.0	0.003	0.004	0.11
0.0566	40.0	2520	3.6648	0.503	0.029	0.0	0.0	0.004	0.0	0.003	0.004	0.117

Framework versions

• Transformers 4.37.0
• Pytorch 2.2.1+cu121
• Datasets 2.20.0
• Tokenizers 0.15.2