metadata
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-SQL
results: []
phi-1_5-finetuned-SQL
This model is a fine-tuned version of microsoft/phi-1_5 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1735
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 24000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.3757 | 0.04 | 100 | 2.0747 |
| 2.0269 | 0.08 | 200 | 1.9990 |
| 1.9535 | 0.12 | 300 | 1.9450 |
| 1.9136 | 0.16 | 400 | 1.9067 |
| 1.892 | 0.2 | 500 | 1.8757 |
| 1.8753 | 0.24 | 600 | 1.8574 |
| 1.8507 | 0.28 | 700 | 1.8359 |
| 1.8759 | 0.32 | 800 | 1.8167 |
| 1.8166 | 0.36 | 900 | 1.8054 |
| 1.8224 | 0.4 | 1000 | 1.7818 |
| 1.7852 | 0.44 | 1100 | 1.7814 |
| 1.8164 | 0.48 | 1200 | 1.7664 |
| 1.7632 | 0.52 | 1300 | 1.7598 |
| 1.8485 | 0.56 | 1400 | 1.7439 |
| 1.7712 | 0.6 | 1500 | 1.7303 |
| 1.7632 | 0.64 | 1600 | 1.7277 |
| 1.7378 | 0.68 | 1700 | 1.7135 |
| 1.7581 | 0.72 | 1800 | 1.7075 |
| 1.7261 | 0.76 | 1900 | 1.6933 |
| 1.7243 | 0.8 | 2000 | 1.6891 |
| 1.7311 | 0.84 | 2100 | 1.6837 |
| 1.7554 | 0.88 | 2200 | 1.6808 |
| 1.7026 | 0.92 | 2300 | 1.6646 |
| 1.7193 | 0.96 | 2400 | 1.6664 |
| 1.6861 | 1.0 | 2500 | 1.6577 |
| 1.68 | 1.04 | 2600 | 1.6470 |
| 1.5931 | 1.08 | 2700 | 1.6425 |
| 1.6655 | 1.12 | 2800 | 1.6352 |
| 1.629 | 1.16 | 2900 | 1.6298 |
| 1.6567 | 1.2 | 3000 | 1.6236 |
| 1.6225 | 1.24 | 3100 | 1.6242 |
| 1.6249 | 1.28 | 3200 | 1.6150 |
| 1.6263 | 1.32 | 3300 | 1.6077 |
| 1.6055 | 1.36 | 3400 | 1.6034 |
| 1.6338 | 1.4 | 3500 | 1.5996 |
| 1.6032 | 1.44 | 3600 | 1.5947 |
| 1.6447 | 1.48 | 3700 | 1.5882 |
| 1.6063 | 1.52 | 3800 | 1.5877 |
| 1.5933 | 1.56 | 3900 | 1.5850 |
| 1.6267 | 1.6 | 4000 | 1.5814 |
| 1.6151 | 1.64 | 4100 | 1.5709 |
| 1.6047 | 1.68 | 4200 | 1.5683 |
| 1.5811 | 1.72 | 4300 | 1.5661 |
| 1.5877 | 1.76 | 4400 | 1.5648 |
| 1.6321 | 1.8 | 4500 | 1.5645 |
| 1.5969 | 1.84 | 4600 | 1.5584 |
| 1.5971 | 1.88 | 4700 | 1.5565 |
| 1.622 | 1.92 | 4800 | 1.5547 |
| 1.6265 | 1.96 | 4900 | 1.5496 |
| 1.6145 | 2.0 | 5000 | 1.5466 |
| 1.526 | 2.04 | 5100 | 1.5427 |
| 1.5793 | 2.08 | 5200 | 1.5390 |
| 1.5714 | 2.12 | 5300 | 1.5375 |
| 1.5228 | 2.16 | 5400 | 1.5360 |
| 1.5383 | 2.2 | 5500 | 1.5343 |
| 1.5117 | 2.24 | 5600 | 1.5322 |
| 1.5427 | 2.28 | 5700 | 1.5316 |
| 1.4959 | 2.32 | 5800 | 1.5306 |
| 1.5456 | 2.36 | 5900 | 1.5299 |
| 1.5175 | 2.4 | 6000 | 1.5295 |
| 1.5823 | 2.44 | 6100 | 1.5498 |
| 1.5615 | 2.48 | 6200 | 1.5447 |
| 1.5326 | 2.52 | 6300 | 1.5463 |
| 1.567 | 2.56 | 6400 | 1.5450 |
| 1.5243 | 2.6 | 6500 | 1.5456 |
| 1.5214 | 2.64 | 6600 | 1.5383 |
| 1.6086 | 2.68 | 6700 | 1.5393 |
| 1.5391 | 2.72 | 6800 | 1.5285 |
| 1.5224 | 2.76 | 6900 | 1.5318 |
| 1.5567 | 2.8 | 7000 | 1.5292 |
| 1.5525 | 2.84 | 7100 | 1.5207 |
| 1.5399 | 2.88 | 7200 | 1.5135 |
| 1.5399 | 2.92 | 7300 | 1.5104 |
| 1.5765 | 2.96 | 7400 | 1.5085 |
| 1.556 | 3.0 | 7500 | 1.5042 |
| 1.4977 | 3.04 | 7600 | 1.4997 |
| 1.4818 | 3.08 | 7700 | 1.4930 |
| 1.4912 | 3.12 | 7800 | 1.4908 |
| 1.517 | 3.16 | 7900 | 1.4933 |
| 1.4971 | 3.2 | 8000 | 1.4857 |
| 1.4827 | 3.24 | 8100 | 1.4805 |
| 1.5096 | 3.28 | 8200 | 1.4804 |
| 1.4788 | 3.32 | 8300 | 1.4756 |
| 1.457 | 3.36 | 8400 | 1.4728 |
| 1.4819 | 3.4 | 8500 | 1.4717 |
| 1.5241 | 3.44 | 8600 | 1.4678 |
| 1.5081 | 3.48 | 8700 | 1.4676 |
| 1.5173 | 3.52 | 8800 | 1.4657 |
| 1.4765 | 3.56 | 8900 | 1.4643 |
| 1.4691 | 3.6 | 9000 | 1.4603 |
| 1.5034 | 3.64 | 9100 | 1.4577 |
| 1.4997 | 3.68 | 9200 | 1.4552 |
| 1.4849 | 3.72 | 9300 | 1.4504 |
| 1.5144 | 3.76 | 9400 | 1.4518 |
| 1.4972 | 3.8 | 9500 | 1.4469 |
| 1.4695 | 3.84 | 9600 | 1.4474 |
| 1.5088 | 3.88 | 9700 | 1.4468 |
| 1.4772 | 3.92 | 9800 | 1.4418 |
| 1.5207 | 3.96 | 9900 | 1.4390 |
| 1.5088 | 4.0 | 10000 | 1.4378 |
| 1.4915 | 4.04 | 10100 | 1.4324 |
| 1.4356 | 4.08 | 10200 | 1.4305 |
| 1.4388 | 4.12 | 10300 | 1.4268 |
| 1.4004 | 4.16 | 10400 | 1.4251 |
| 1.3909 | 4.2 | 10500 | 1.4225 |
| 1.4284 | 4.24 | 10600 | 1.4218 |
| 1.4422 | 4.28 | 10700 | 1.4213 |
| 1.4301 | 4.32 | 10800 | 1.4198 |
| 1.4309 | 4.36 | 10900 | 1.4174 |
| 1.415 | 4.4 | 11000 | 1.4147 |
| 1.4697 | 4.44 | 11100 | 1.4136 |
| 1.4241 | 4.48 | 11200 | 1.4123 |
| 1.4416 | 4.52 | 11300 | 1.4100 |
| 1.4229 | 4.56 | 11400 | 1.4094 |
| 1.4498 | 4.6 | 11500 | 1.4091 |
| 1.4023 | 4.64 | 11600 | 1.4083 |
| 1.4197 | 4.68 | 11700 | 1.4075 |
| 1.4165 | 4.72 | 11800 | 1.4070 |
| 1.4103 | 4.76 | 11900 | 1.4067 |
| 1.4214 | 4.8 | 12000 | 1.4066 |
| 1.4223 | 9.68 | 12100 | 1.4162 |
| 1.4471 | 9.76 | 12200 | 1.4210 |
| 1.4165 | 9.84 | 12300 | 1.4154 |
| 1.4088 | 9.92 | 12400 | 1.4105 |
| 1.4057 | 10.0 | 12500 | 1.4100 |
| 1.3778 | 10.08 | 12600 | 1.4034 |
| 1.4081 | 10.16 | 12700 | 1.4055 |
| 1.4127 | 10.24 | 12800 | 1.4001 |
| 1.4282 | 10.32 | 12900 | 1.3924 |
| 1.4069 | 10.4 | 13000 | 1.3909 |
| 1.4097 | 10.48 | 13100 | 1.3885 |
| 1.4173 | 10.56 | 13200 | 1.3824 |
| 1.4282 | 10.64 | 13300 | 1.3798 |
| 1.4266 | 10.72 | 13400 | 1.3778 |
| 1.4205 | 10.8 | 13500 | 1.3760 |
| 1.4347 | 10.88 | 13600 | 1.3730 |
| 1.4088 | 10.96 | 13700 | 1.3659 |
| 1.3859 | 11.04 | 13800 | 1.3605 |
| 1.3711 | 11.12 | 13900 | 1.3572 |
| 1.3896 | 11.2 | 14000 | 1.3550 |
| 1.343 | 11.28 | 14100 | 1.3510 |
| 1.3866 | 11.36 | 14200 | 1.3485 |
| 1.3603 | 11.44 | 14300 | 1.3468 |
| 1.3881 | 11.52 | 14400 | 1.3448 |
| 1.3841 | 11.6 | 14500 | 1.3422 |
| 1.358 | 11.68 | 14600 | 1.3379 |
| 1.3704 | 11.76 | 14700 | 1.3352 |
| 1.3656 | 11.84 | 14800 | 1.3350 |
| 1.367 | 11.92 | 14900 | 1.3299 |
| 1.3765 | 12.0 | 15000 | 1.3302 |
| 1.32 | 12.08 | 15100 | 1.3240 |
| 1.343 | 12.16 | 15200 | 1.3186 |
| 1.3254 | 12.24 | 15300 | 1.3159 |
| 1.3433 | 12.32 | 15400 | 1.3134 |
| 1.3347 | 12.4 | 15500 | 1.3113 |
| 1.3304 | 12.48 | 15600 | 1.3110 |
| 1.3235 | 12.56 | 15700 | 1.3106 |
| 1.3099 | 12.64 | 15800 | 1.3056 |
| 1.3176 | 12.72 | 15900 | 1.3027 |
| 1.3613 | 12.8 | 16000 | 1.3057 |
| 1.3238 | 12.88 | 16100 | 1.3006 |
| 1.354 | 12.96 | 16200 | 1.3003 |
| 1.3324 | 13.04 | 16300 | 1.2967 |
| 1.322 | 13.12 | 16400 | 1.2945 |
| 1.3029 | 13.2 | 16500 | 1.2898 |
| 1.317 | 13.28 | 16600 | 1.2892 |
| 1.2982 | 13.36 | 16700 | 1.2882 |
| 1.3092 | 13.44 | 16800 | 1.2878 |
| 1.3161 | 13.52 | 16900 | 1.2866 |
| 1.2895 | 13.6 | 17000 | 1.2844 |
| 1.28 | 13.68 | 17100 | 1.2834 |
| 1.2849 | 13.76 | 17200 | 1.2822 |
| 1.3136 | 13.84 | 17300 | 1.2828 |
| 1.2938 | 13.92 | 17400 | 1.2810 |
| 1.2994 | 14.0 | 17500 | 1.2803 |
| 1.3158 | 14.08 | 17600 | 1.2788 |
| 1.2783 | 14.16 | 17700 | 1.2779 |
| 1.2811 | 14.24 | 17800 | 1.2774 |
| 1.2824 | 14.32 | 17900 | 1.2771 |
| 1.2881 | 14.4 | 18000 | 1.2770 |
| 1.2971 | 14.48 | 18100 | 1.2880 |
| 1.2878 | 14.56 | 18200 | 1.2883 |
| 1.3081 | 14.64 | 18300 | 1.2812 |
| 1.2949 | 14.72 | 18400 | 1.2812 |
| 1.3153 | 14.8 | 18500 | 1.2827 |
| 1.3316 | 14.88 | 18600 | 1.2777 |
| 1.3225 | 14.96 | 18700 | 1.2789 |
| 1.3022 | 15.04 | 18800 | 1.2719 |
| 1.2773 | 15.12 | 18900 | 1.2685 |
| 1.2787 | 15.2 | 19000 | 1.2674 |
| 1.2876 | 15.28 | 19100 | 1.2644 |
| 1.2801 | 15.36 | 19200 | 1.2630 |
| 1.3197 | 15.44 | 19300 | 1.2615 |
| 1.2968 | 15.52 | 19400 | 1.2572 |
| 1.2992 | 15.6 | 19500 | 1.2581 |
| 1.2739 | 15.68 | 19600 | 1.2511 |
| 1.2925 | 15.76 | 19700 | 1.2485 |
| 1.2831 | 15.84 | 19800 | 1.2456 |
| 1.3055 | 15.92 | 19900 | 1.2415 |
| 1.2883 | 16.0 | 20000 | 1.2432 |
| 1.2378 | 16.08 | 20100 | 1.2358 |
| 1.2618 | 16.16 | 20200 | 1.2354 |
| 1.2475 | 16.24 | 20300 | 1.2294 |
| 1.2534 | 16.32 | 20400 | 1.2267 |
| 1.2362 | 16.4 | 20500 | 1.2249 |
| 1.2442 | 16.48 | 20600 | 1.2245 |
| 1.2727 | 16.56 | 20700 | 1.2209 |
| 1.2645 | 16.64 | 20800 | 1.2192 |
| 1.2535 | 16.72 | 20900 | 1.2158 |
| 1.2673 | 16.8 | 21000 | 1.2131 |
| 1.2693 | 16.88 | 21100 | 1.2133 |
| 1.2419 | 16.96 | 21200 | 1.2104 |
| 1.2165 | 17.04 | 21300 | 1.2064 |
| 1.2184 | 17.12 | 21400 | 1.2047 |
| 1.2195 | 17.2 | 21500 | 1.2036 |
| 1.2126 | 17.28 | 21600 | 1.2024 |
| 1.2048 | 17.36 | 21700 | 1.1989 |
| 1.2158 | 17.44 | 21800 | 1.1991 |
| 1.2372 | 17.52 | 21900 | 1.1966 |
| 1.2502 | 17.6 | 22000 | 1.1964 |
| 1.23 | 17.68 | 22100 | 1.1924 |
| 1.1967 | 17.76 | 22200 | 1.1913 |
| 1.2021 | 17.84 | 22300 | 1.1896 |
| 1.2323 | 17.92 | 22400 | 1.1904 |
| 1.2276 | 18.0 | 22500 | 1.1872 |
| 1.2072 | 18.08 | 22600 | 1.1851 |
| 1.157 | 18.16 | 22700 | 1.1828 |
| 1.1805 | 18.24 | 22800 | 1.1827 |
| 1.1812 | 18.32 | 22900 | 1.1812 |
| 1.1993 | 18.4 | 23000 | 1.1800 |
| 1.1887 | 18.48 | 23100 | 1.1803 |
| 1.194 | 18.56 | 23200 | 1.1779 |
| 1.2097 | 18.64 | 23300 | 1.1777 |
| 1.2049 | 18.72 | 23400 | 1.1769 |
| 1.2002 | 18.8 | 23500 | 1.1758 |
| 1.2178 | 18.88 | 23600 | 1.1755 |
| 1.1969 | 18.96 | 23700 | 1.1745 |
| 1.198 | 19.04 | 23800 | 1.1741 |
| 1.1919 | 19.12 | 23900 | 1.1736 |
| 1.149 | 19.2 | 24000 | 1.1735 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1