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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
