results

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4335
  • F1: 0.8190

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: 2e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 70
  • num_epochs: 4
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss F1
1.0065 0.0684 16 0.9778 0.5401
1.005 0.1368 32 0.9209 0.5508
0.8912 0.2051 48 0.8197 0.5698
0.8738 0.2735 64 0.7217 0.5946
0.6965 0.3419 80 0.6439 0.6593
0.6463 0.4103 96 0.6081 0.6828
0.6129 0.4786 112 0.5541 0.7278
0.5931 0.5470 128 0.5693 0.6868
0.5643 0.6154 144 0.5290 0.7454
0.5601 0.6838 160 0.5402 0.7159
0.5259 0.7521 176 0.5021 0.7613
0.5361 0.8205 192 0.5051 0.7653
0.5235 0.8889 208 0.4816 0.7747
0.526 0.9573 224 0.4726 0.7765
0.486 1.0256 240 0.4786 0.7712
0.4757 1.0940 256 0.4669 0.7804
0.4635 1.1624 272 0.4682 0.7891
0.4691 1.2308 288 0.4561 0.7898
0.4682 1.2991 304 0.4818 0.7542
0.4229 1.3675 320 0.4704 0.7831
0.4192 1.4359 336 0.4544 0.7964
0.4249 1.5043 352 0.4493 0.7928
0.4339 1.5726 368 0.4597 0.7921
0.4513 1.6410 384 0.4478 0.7931
0.4553 1.7094 400 0.4474 0.7916
0.42 1.7778 416 0.4473 0.7917
0.4194 1.8462 432 0.4416 0.8002
0.4265 1.9145 448 0.4370 0.8054
0.4216 1.9829 464 0.4324 0.8117
0.3869 2.0513 480 0.4369 0.8010
0.3617 2.1197 496 0.4424 0.8096
0.3773 2.1880 512 0.4558 0.8042
0.3852 2.2564 528 0.4311 0.8109
0.3726 2.3248 544 0.4403 0.8096
0.3586 2.3932 560 0.4381 0.8125
0.3756 2.4615 576 0.4337 0.8109
0.3765 2.5299 592 0.4341 0.8110
0.4104 2.5983 608 0.4263 0.8120
0.3704 2.6667 624 0.4404 0.8063
0.4087 2.7350 640 0.4271 0.8171
0.3498 2.8034 656 0.4336 0.8162
0.3606 2.8718 672 0.4286 0.8180
0.343 2.9402 688 0.4343 0.8039
0.378 3.0085 704 0.4491 0.8018
0.3199 3.0769 720 0.4344 0.8131
0.3529 3.1453 736 0.4332 0.8148
0.3228 3.2137 752 0.4362 0.8170
0.3061 3.2821 768 0.4390 0.8162
0.3277 3.3504 784 0.4385 0.8170
0.2973 3.4188 800 0.4389 0.8143
0.3162 3.4872 816 0.4348 0.8181
0.3078 3.5556 832 0.4345 0.8171
0.3482 3.6239 848 0.4359 0.8125
0.3243 3.6923 864 0.4336 0.8170
0.3465 3.7607 880 0.4337 0.8175
0.2984 3.8291 896 0.4329 0.8194
0.3159 3.8974 912 0.4332 0.8190
0.3327 3.9658 928 0.4335 0.8190

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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