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Llama-2-7b-hf-finetuned-mrpc-v3
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: 0.6823
- Accuracy: 0.7475
- F1: 0.8245
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: 0.0001
- 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 | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 230 | 0.6528 | 0.625 | 0.6982 |
| No log | 2.0 | 460 | 0.6217 | 0.6936 | 0.8159 |
| 0.6443 | 3.0 | 690 | 0.6033 | 0.6985 | 0.7993 |
| 0.6443 | 4.0 | 920 | 0.6240 | 0.6838 | 0.8089 |
| 0.6173 | 5.0 | 1150 | 0.5451 | 0.7255 | 0.8170 |
| 0.6173 | 6.0 | 1380 | 0.5380 | 0.7451 | 0.8188 |
| 0.5776 | 7.0 | 1610 | 0.5376 | 0.7426 | 0.8346 |
| 0.5776 | 8.0 | 1840 | 0.5518 | 0.7230 | 0.8243 |
| 0.5353 | 9.0 | 2070 | 0.5270 | 0.7475 | 0.8325 |
| 0.5353 | 10.0 | 2300 | 0.5381 | 0.7377 | 0.8086 |
| 0.5071 | 11.0 | 2530 | 0.5453 | 0.7181 | 0.7842 |
| 0.5071 | 12.0 | 2760 | 0.5335 | 0.7475 | 0.8341 |
| 0.5071 | 13.0 | 2990 | 0.5617 | 0.7083 | 0.7733 |
| 0.492 | 14.0 | 3220 | 0.5343 | 0.7426 | 0.8115 |
| 0.492 | 15.0 | 3450 | 0.5133 | 0.7696 | 0.8423 |
| 0.4608 | 16.0 | 3680 | 0.5573 | 0.7549 | 0.8366 |
| 0.4608 | 17.0 | 3910 | 0.5282 | 0.7721 | 0.8447 |
| 0.4283 | 18.0 | 4140 | 0.5894 | 0.7132 | 0.7710 |
| 0.4283 | 19.0 | 4370 | 0.5875 | 0.7328 | 0.8239 |
| 0.4042 | 20.0 | 4600 | 0.5447 | 0.7647 | 0.8339 |
| 0.4042 | 21.0 | 4830 | 0.5712 | 0.7598 | 0.8399 |
| 0.3904 | 22.0 | 5060 | 0.5563 | 0.7623 | 0.8301 |
| 0.3904 | 23.0 | 5290 | 0.5718 | 0.7623 | 0.8364 |
| 0.3597 | 24.0 | 5520 | 0.5592 | 0.7525 | 0.8250 |
| 0.3597 | 25.0 | 5750 | 0.5941 | 0.7574 | 0.8364 |
| 0.3597 | 26.0 | 5980 | 0.5811 | 0.7623 | 0.8370 |
| 0.3445 | 27.0 | 6210 | 0.6083 | 0.7549 | 0.8339 |
| 0.3445 | 28.0 | 6440 | 0.6049 | 0.75 | 0.8265 |
| 0.3197 | 29.0 | 6670 | 0.6042 | 0.7549 | 0.8311 |
| 0.3197 | 30.0 | 6900 | 0.6260 | 0.7377 | 0.8099 |
| 0.3 | 31.0 | 7130 | 0.6438 | 0.75 | 0.8229 |
| 0.3 | 32.0 | 7360 | 0.6319 | 0.7402 | 0.8233 |
| 0.2873 | 33.0 | 7590 | 0.6502 | 0.7402 | 0.8191 |
| 0.2873 | 34.0 | 7820 | 0.6591 | 0.7426 | 0.8187 |
| 0.2719 | 35.0 | 8050 | 0.6474 | 0.7451 | 0.8219 |
| 0.2719 | 36.0 | 8280 | 0.6803 | 0.7598 | 0.8367 |
| 0.2583 | 37.0 | 8510 | 0.6903 | 0.7475 | 0.8221 |
| 0.2583 | 38.0 | 8740 | 0.6965 | 0.7525 | 0.8279 |
| 0.2583 | 39.0 | 8970 | 0.6850 | 0.75 | 0.8235 |
| 0.2423 | 40.0 | 9200 | 0.6823 | 0.7475 | 0.8245 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
