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EricWesthoff
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phi-1_5-finetuned-SQL

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mixformer-sequential
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@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.2630
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  ## Model description
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@@ -39,373 +39,73 @@ The following hyperparameters were used during training:
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  - eval_batch_size: 4
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: cosine
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- - training_steps: 36000
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss |
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- |:-------------:|:-----:|:-----:|:---------------:|
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- | 2.4485 | 0.4 | 100 | 2.0478 |
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- | 2.0521 | 0.8 | 200 | 1.9223 |
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- | 1.9626 | 1.2 | 300 | 1.8386 |
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- | 1.8707 | 1.6 | 400 | 1.7702 |
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- | 1.79 | 2.0 | 500 | 1.7149 |
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- | 1.7197 | 2.4 | 600 | 1.6567 |
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- | 1.6904 | 2.8 | 700 | 1.6055 |
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- | 1.6379 | 3.2 | 800 | 1.5583 |
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- | 1.5794 | 3.6 | 900 | 1.5267 |
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- | 1.5977 | 4.0 | 1000 | 1.4928 |
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- | 1.4773 | 4.4 | 1100 | 1.4638 |
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- | 1.5185 | 4.8 | 1200 | 1.4446 |
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- | 1.4476 | 5.2 | 1300 | 1.4337 |
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- | 1.4321 | 5.6 | 1400 | 1.4287 |
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- | 1.4393 | 6.0 | 1500 | 1.4282 |
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- | 1.4956 | 6.4 | 1600 | 1.4504 |
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- | 1.5252 | 6.8 | 1700 | 1.4311 |
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- | 1.4864 | 7.2 | 1800 | 1.3654 |
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- | 1.4092 | 7.6 | 1900 | 1.3112 |
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- | 1.4063 | 8.0 | 2000 | 1.2925 |
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- | 1.2657 | 8.4 | 2100 | 1.2123 |
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- | 1.312 | 8.8 | 2200 | 1.1824 |
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- | 1.2451 | 9.2 | 2300 | 1.1223 |
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- | 1.1777 | 9.6 | 2400 | 1.0857 |
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- | 1.1913 | 10.0 | 2500 | 1.0422 |
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- | 1.0452 | 10.4 | 2600 | 0.9842 |
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- | 1.082 | 10.8 | 2700 | 0.9442 |
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- | 0.9814 | 11.2 | 2800 | 0.9002 |
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- | 0.9496 | 11.6 | 2900 | 0.8559 |
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- | 0.9639 | 12.0 | 3000 | 0.8163 |
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- | 0.823 | 12.4 | 3100 | 0.7827 |
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- | 0.8395 | 12.8 | 3200 | 0.7384 |
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- | 0.8038 | 13.2 | 3300 | 0.6971 |
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- | 0.7458 | 13.6 | 3400 | 0.6641 |
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- | 0.7495 | 14.0 | 3500 | 0.6328 |
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- | 0.6575 | 14.4 | 3600 | 0.6017 |
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- | 0.6448 | 14.8 | 3700 | 0.5829 |
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- | 0.6268 | 15.2 | 3800 | 0.5412 |
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- | 0.5738 | 15.6 | 3900 | 0.5233 |
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- | 0.5989 | 16.0 | 4000 | 0.5008 |
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- | 0.5033 | 16.4 | 4100 | 0.4781 |
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- | 0.5343 | 16.8 | 4200 | 0.4572 |
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- | 0.4881 | 17.2 | 4300 | 0.4390 |
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- | 0.4676 | 17.6 | 4400 | 0.4254 |
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- | 0.4683 | 18.0 | 4500 | 0.4171 |
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- | 0.4188 | 18.4 | 4600 | 0.3987 |
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- | 0.4245 | 18.8 | 4700 | 0.3869 |
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- | 0.4136 | 19.2 | 4800 | 0.3777 |
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- | 0.3938 | 19.6 | 4900 | 0.3694 |
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- | 0.3986 | 20.0 | 5000 | 0.3627 |
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- | 0.3661 | 20.4 | 5100 | 0.3571 |
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- | 0.3743 | 20.8 | 5200 | 0.3516 |
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- | 0.3668 | 21.2 | 5300 | 0.3482 |
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- | 0.3613 | 21.6 | 5400 | 0.3455 |
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- | 0.3542 | 22.0 | 5500 | 0.3430 |
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- | 0.3505 | 22.4 | 5600 | 0.3419 |
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- | 0.3495 | 22.8 | 5700 | 0.3410 |
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- | 0.3396 | 23.2 | 5800 | 0.3405 |
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- | 0.3481 | 23.6 | 5900 | 0.3403 |
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- | 0.3444 | 24.0 | 6000 | 0.3403 |
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- | 0.4918 | 24.4 | 6100 | 0.4983 |
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- | 0.5913 | 24.8 | 6200 | 0.4897 |
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- | 0.5565 | 25.2 | 6300 | 0.4776 |
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- | 0.5439 | 25.6 | 6400 | 0.4586 |
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- | 0.5586 | 26.0 | 6500 | 0.4355 |
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- | 0.4542 | 26.4 | 6600 | 0.4205 |
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- | 0.4895 | 26.8 | 6700 | 0.3966 |
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- | 0.4576 | 27.2 | 6800 | 0.3798 |
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- | 0.4252 | 27.6 | 6900 | 0.3597 |
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- | 0.4427 | 28.0 | 7000 | 0.3365 |
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- | 0.3589 | 28.4 | 7100 | 0.3258 |
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- | 0.3888 | 28.8 | 7200 | 0.3280 |
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- | 0.3662 | 29.2 | 7300 | 0.3129 |
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- | 0.3422 | 29.6 | 7400 | 0.2991 |
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- | 0.3604 | 30.0 | 7500 | 0.2811 |
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- | 0.3039 | 30.4 | 7600 | 0.2861 |
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- | 0.3268 | 30.8 | 7700 | 0.2752 |
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- | 0.3087 | 31.2 | 7800 | 0.2687 |
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- | 0.3067 | 31.6 | 7900 | 0.2662 |
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- | 0.3044 | 32.0 | 8000 | 0.2558 |
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- | 0.2737 | 32.4 | 8100 | 0.2558 |
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- | 0.2903 | 32.8 | 8200 | 0.2517 |
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- | 0.2744 | 33.2 | 8300 | 0.2482 |
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- | 0.2757 | 33.6 | 8400 | 0.2435 |
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- | 0.2771 | 34.0 | 8500 | 0.2360 |
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- | 0.2488 | 34.4 | 8600 | 0.2393 |
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- | 0.266 | 34.8 | 8700 | 0.2341 |
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- | 0.2536 | 35.2 | 8800 | 0.2312 |
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- | 0.2516 | 35.6 | 8900 | 0.2288 |
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- | 0.2575 | 36.0 | 9000 | 0.2242 |
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- | 0.2358 | 36.4 | 9100 | 0.2268 |
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- | 0.2489 | 36.8 | 9200 | 0.2204 |
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- | 0.2335 | 37.2 | 9300 | 0.2196 |
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- | 0.2381 | 37.6 | 9400 | 0.2170 |
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- | 0.2428 | 38.0 | 9500 | 0.2142 |
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- | 0.2235 | 38.4 | 9600 | 0.2158 |
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- | 0.2392 | 38.8 | 9700 | 0.2126 |
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- | 0.2221 | 39.2 | 9800 | 0.2113 |
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- | 0.2247 | 39.6 | 9900 | 0.2094 |
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- | 0.2341 | 40.0 | 10000 | 0.2067 |
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- | 0.2136 | 40.4 | 10100 | 0.2065 |
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- | 0.2256 | 40.8 | 10200 | 0.2046 |
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- | 0.22 | 41.2 | 10300 | 0.2034 |
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- | 0.2144 | 41.6 | 10400 | 0.2032 |
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- | 0.224 | 42.0 | 10500 | 0.2006 |
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- | 0.2101 | 42.4 | 10600 | 0.2006 |
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- | 0.2136 | 42.8 | 10700 | 0.1992 |
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- | 0.2171 | 43.2 | 10800 | 0.1982 |
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- | 0.2077 | 43.6 | 10900 | 0.2003 |
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- | 0.217 | 44.0 | 11000 | 0.1979 |
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- | 0.2036 | 44.4 | 11100 | 0.1983 |
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- | 0.2083 | 44.8 | 11200 | 0.1970 |
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- | 0.2134 | 45.2 | 11300 | 0.1961 |
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- | 0.2071 | 45.6 | 11400 | 0.1943 |
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- | 0.2115 | 46.0 | 11500 | 0.1937 |
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- | 0.1997 | 46.4 | 11600 | 0.1952 |
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- | 0.2055 | 46.8 | 11700 | 0.1932 |
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- | 0.2057 | 47.2 | 11800 | 0.1926 |
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- | 0.2011 | 47.6 | 11900 | 0.1932 |
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- | 0.2092 | 48.0 | 12000 | 0.1908 |
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- | 0.1934 | 48.4 | 12100 | 0.1918 |
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- | 0.2065 | 48.8 | 12200 | 0.1915 |
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- | 0.2009 | 49.2 | 12300 | 0.1911 |
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- | 0.1995 | 49.6 | 12400 | 0.1904 |
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- | 0.205 | 50.0 | 12500 | 0.1889 |
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- | 0.1925 | 50.4 | 12600 | 0.1892 |
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- | 0.2013 | 50.8 | 12700 | 0.1886 |
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- | 0.1955 | 51.2 | 12800 | 0.1883 |
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- | 0.1989 | 51.6 | 12900 | 0.1880 |
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- | 0.1982 | 52.0 | 13000 | 0.1872 |
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- | 0.1872 | 52.4 | 13100 | 0.1878 |
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- | 0.1984 | 52.8 | 13200 | 0.1868 |
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- | 0.1974 | 53.2 | 13300 | 0.1871 |
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- | 0.188 | 53.6 | 13400 | 0.1871 |
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- | 0.2026 | 54.0 | 13500 | 0.1860 |
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- | 0.1919 | 54.4 | 13600 | 0.1863 |
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- | 0.1946 | 54.8 | 13700 | 0.1852 |
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- | 0.19 | 55.2 | 13800 | 0.1851 |
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- | 0.1915 | 55.6 | 13900 | 0.1852 |
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- | 0.1962 | 56.0 | 14000 | 0.1845 |
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- | 0.1922 | 56.4 | 14100 | 0.1851 |
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- | 0.1901 | 56.8 | 14200 | 0.1851 |
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- | 0.1896 | 57.2 | 14300 | 0.1839 |
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- | 0.1888 | 57.6 | 14400 | 0.1840 |
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- | 0.1921 | 58.0 | 14500 | 0.1838 |
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- | 0.1856 | 58.4 | 14600 | 0.1836 |
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- | 0.1902 | 58.8 | 14700 | 0.1832 |
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- | 0.1879 | 59.2 | 14800 | 0.1830 |
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- | 0.1868 | 59.6 | 14900 | 0.1832 |
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- | 0.1931 | 60.0 | 15000 | 0.1827 |
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- | 0.1881 | 60.4 | 15100 | 0.1830 |
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- | 0.1856 | 60.8 | 15200 | 0.1825 |
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- | 0.1876 | 61.2 | 15300 | 0.1826 |
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- | 0.1851 | 61.6 | 15400 | 0.1823 |
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- | 0.1862 | 62.0 | 15500 | 0.1821 |
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- | 0.1844 | 62.4 | 15600 | 0.1824 |
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- | 0.1879 | 62.8 | 15700 | 0.1819 |
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- | 0.1826 | 63.2 | 15800 | 0.1819 |
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- | 0.1844 | 63.6 | 15900 | 0.1818 |
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- | 0.1861 | 64.0 | 16000 | 0.1816 |
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- | 0.1815 | 64.4 | 16100 | 0.1817 |
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- | 0.1822 | 64.8 | 16200 | 0.1816 |
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- | 0.1861 | 65.2 | 16300 | 0.1816 |
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- | 0.1828 | 65.6 | 16400 | 0.1815 |
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- | 0.1852 | 66.0 | 16500 | 0.1814 |
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- | 0.182 | 66.4 | 16600 | 0.1814 |
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- | 0.1843 | 66.8 | 16700 | 0.1814 |
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- | 0.181 | 67.2 | 16800 | 0.1813 |
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- | 0.1811 | 67.6 | 16900 | 0.1813 |
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- | 0.1846 | 68.0 | 17000 | 0.1813 |
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- | 0.1801 | 68.4 | 17100 | 0.1813 |
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- | 0.1837 | 68.8 | 17200 | 0.1813 |
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- | 0.1826 | 69.2 | 17300 | 0.1812 |
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- | 0.1831 | 69.6 | 17400 | 0.1812 |
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- | 0.1801 | 70.0 | 17500 | 0.1812 |
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- | 0.1789 | 70.4 | 17600 | 0.1812 |
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- | 0.1827 | 70.8 | 17700 | 0.1812 |
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- | 0.1832 | 71.2 | 17800 | 0.1812 |
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- | 0.1818 | 71.6 | 17900 | 0.1812 |
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- | 0.181 | 72.0 | 18000 | 0.1812 |
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- | 2.0915 | 1.46 | 18100 | 1.3624 |
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- | 1.8647 | 1.47 | 18200 | 1.3663 |
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- | 1.8362 | 1.48 | 18300 | 1.3781 |
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- | 1.8216 | 1.49 | 18400 | 1.3598 |
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- | 1.8023 | 1.5 | 18500 | 1.3633 |
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- | 1.7273 | 1.5 | 18600 | 1.3409 |
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- | 1.7835 | 1.51 | 18700 | 1.3696 |
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- | 1.8034 | 1.52 | 18800 | 1.3512 |
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- | 1.7312 | 1.53 | 18900 | 1.3322 |
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- | 1.7479 | 1.54 | 19000 | 1.3300 |
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- | 1.6961 | 1.54 | 19100 | 1.3488 |
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- | 1.7625 | 1.55 | 19200 | 1.3566 |
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- | 1.6767 | 1.56 | 19300 | 1.3311 |
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- | 1.7146 | 1.57 | 19400 | 1.3366 |
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- | 1.7105 | 1.58 | 19500 | 1.3369 |
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- | 1.718 | 1.59 | 19600 | 1.3580 |
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- | 1.7224 | 1.59 | 19700 | 1.3446 |
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- | 1.6981 | 1.6 | 19800 | 1.3481 |
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- | 1.6872 | 1.61 | 19900 | 1.3515 |
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- | 1.6453 | 1.62 | 20000 | 1.3442 |
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- | 1.7233 | 1.63 | 20100 | 1.3501 |
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- | 1.7092 | 1.63 | 20200 | 1.3388 |
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- | 1.6792 | 1.64 | 20300 | 1.3404 |
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- | 1.7033 | 1.65 | 20400 | 1.3280 |
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- | 1.6514 | 1.66 | 20500 | 1.3296 |
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- | 1.6873 | 1.67 | 20600 | 1.3415 |
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- | 1.7064 | 1.67 | 20700 | 1.3384 |
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- | 1.6438 | 1.68 | 20800 | 1.3372 |
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- | 1.6821 | 1.69 | 20900 | 1.3414 |
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- | 1.6491 | 1.7 | 21000 | 1.3356 |
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- | 1.7099 | 1.71 | 21100 | 1.3436 |
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- | 1.6279 | 1.71 | 21200 | 1.3265 |
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- | 1.6267 | 1.72 | 21300 | 1.3454 |
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- | 1.6631 | 1.73 | 21400 | 1.3322 |
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- | 1.6078 | 1.74 | 21500 | 1.3367 |
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- | 1.6165 | 1.75 | 21600 | 1.3439 |
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- | 1.6093 | 1.76 | 21700 | 1.3317 |
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- | 1.6648 | 1.76 | 21800 | 1.3248 |
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- | 1.6071 | 1.77 | 21900 | 1.3200 |
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- | 1.6539 | 1.78 | 22000 | 1.3409 |
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- | 1.6084 | 1.79 | 22100 | 1.3362 |
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- | 1.658 | 1.8 | 22200 | 1.3387 |
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- | 1.5855 | 1.8 | 22300 | 1.3271 |
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- | 1.6351 | 1.81 | 22400 | 1.3281 |
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- | 1.6402 | 1.82 | 22500 | 1.3344 |
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- | 1.5961 | 1.83 | 22600 | 1.3247 |
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- | 1.5894 | 1.84 | 22700 | 1.3266 |
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- | 1.6248 | 1.84 | 22800 | 1.3261 |
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- | 1.6172 | 1.85 | 22900 | 1.3210 |
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- | 1.5944 | 1.86 | 23000 | 1.3255 |
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- | 1.6238 | 1.87 | 23100 | 1.3260 |
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- | 1.6705 | 1.88 | 23200 | 1.3198 |
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- | 1.6116 | 1.88 | 23300 | 1.3202 |
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- | 1.5902 | 1.89 | 23400 | 1.3269 |
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- | 1.649 | 1.9 | 23500 | 1.3240 |
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- | 1.5729 | 1.91 | 23600 | 1.3189 |
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- | 1.6074 | 1.92 | 23700 | 1.3283 |
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- | 1.624 | 1.92 | 23800 | 1.3326 |
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- | 1.6319 | 1.93 | 23900 | 1.3282 |
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- | 1.6507 | 1.94 | 24000 | 1.3336 |
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- | 1.6229 | 1.95 | 24100 | 1.3217 |
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- | 1.6241 | 1.96 | 24200 | 1.3226 |
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- | 1.5927 | 1.97 | 24300 | 1.3293 |
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- | 1.5919 | 1.97 | 24400 | 1.3210 |
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- | 1.5779 | 1.98 | 24500 | 1.3222 |
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- | 1.6048 | 1.99 | 24600 | 1.3135 |
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- | 1.6315 | 2.0 | 24700 | 1.3143 |
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- | 1.6103 | 2.01 | 24800 | 1.3141 |
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- | 1.6211 | 2.01 | 24900 | 1.3122 |
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- | 1.5708 | 2.02 | 25000 | 1.3070 |
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- | 1.5982 | 2.03 | 25100 | 1.3040 |
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- | 1.5622 | 2.04 | 25200 | 1.3017 |
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- | 1.5957 | 2.05 | 25300 | 1.2996 |
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- | 1.5581 | 2.05 | 25400 | 1.3034 |
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- | 1.6162 | 2.06 | 25500 | 1.2977 |
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- | 1.615 | 2.07 | 25600 | 1.3019 |
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- | 1.5554 | 2.08 | 25700 | 1.2912 |
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- | 1.6112 | 2.09 | 25800 | 1.2973 |
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- | 1.5937 | 2.09 | 25900 | 1.2989 |
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- | 1.5605 | 2.1 | 26000 | 1.2956 |
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- | 1.5757 | 2.11 | 26100 | 1.2957 |
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- | 1.5362 | 2.12 | 26200 | 1.2945 |
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- | 1.5558 | 2.13 | 26300 | 1.2869 |
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- | 1.5116 | 2.14 | 26400 | 1.2846 |
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- | 1.5563 | 2.14 | 26500 | 1.2931 |
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- | 1.5356 | 2.15 | 26600 | 1.2876 |
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- | 1.5291 | 2.16 | 26700 | 1.2896 |
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- | 1.5452 | 2.17 | 26800 | 1.2835 |
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- | 1.5688 | 2.18 | 26900 | 1.2876 |
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- | 1.5424 | 2.18 | 27000 | 1.2906 |
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- | 1.5295 | 2.19 | 27100 | 1.2862 |
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- | 1.5344 | 2.2 | 27200 | 1.2795 |
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- | 1.5963 | 2.21 | 27300 | 1.2849 |
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- | 1.5569 | 2.22 | 27400 | 1.2857 |
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- | 1.5413 | 2.22 | 27500 | 1.2849 |
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- | 1.5851 | 2.23 | 27600 | 1.2852 |
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- | 1.5496 | 2.24 | 27700 | 1.2855 |
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- | 1.5375 | 2.25 | 27800 | 1.2841 |
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- | 1.5252 | 2.26 | 27900 | 1.2756 |
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- | 1.5657 | 2.26 | 28000 | 1.2853 |
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- | 1.5236 | 2.27 | 28100 | 1.2793 |
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- | 1.5641 | 2.28 | 28200 | 1.2793 |
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- | 1.5485 | 2.29 | 28300 | 1.2799 |
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- | 1.5419 | 2.3 | 28400 | 1.2758 |
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- | 1.5353 | 2.31 | 28500 | 1.2773 |
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- | 1.5716 | 2.31 | 28600 | 1.2792 |
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- | 1.5427 | 2.32 | 28700 | 1.2805 |
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- | 1.5296 | 2.33 | 28800 | 1.2753 |
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- | 1.5551 | 2.34 | 28900 | 1.2759 |
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- | 1.5204 | 2.35 | 29000 | 1.2743 |
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- | 1.575 | 2.35 | 29100 | 1.2740 |
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- | 1.5585 | 2.36 | 29200 | 1.2749 |
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- | 1.547 | 2.37 | 29300 | 1.2724 |
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- | 1.5661 | 2.38 | 29400 | 1.2700 |
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- | 1.4931 | 2.39 | 29500 | 1.2677 |
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- | 1.5507 | 2.39 | 29600 | 1.2703 |
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- | 1.5798 | 2.4 | 29700 | 1.2693 |
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- | 1.5425 | 2.41 | 29800 | 1.2669 |
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- | 1.5636 | 2.42 | 29900 | 1.2731 |
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- | 1.5488 | 2.43 | 30000 | 1.2717 |
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- | 1.5258 | 2.43 | 30100 | 1.2701 |
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- | 1.5395 | 2.44 | 30200 | 1.2676 |
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- | 1.544 | 2.45 | 30300 | 1.2700 |
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- | 1.5259 | 2.46 | 30400 | 1.2674 |
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- | 1.529 | 2.47 | 30500 | 1.2689 |
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- | 1.5162 | 2.47 | 30600 | 1.2651 |
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- | 1.527 | 2.48 | 30700 | 1.2662 |
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- | 1.5273 | 2.49 | 30800 | 1.2670 |
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- | 1.5462 | 2.5 | 30900 | 1.2672 |
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- | 1.5043 | 2.51 | 31000 | 1.2704 |
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- | 1.5811 | 2.52 | 31100 | 1.2708 |
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- | 1.5619 | 2.52 | 31200 | 1.2706 |
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- | 1.5714 | 2.53 | 31300 | 1.2688 |
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- | 1.5901 | 2.54 | 31400 | 1.2723 |
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- | 1.5845 | 2.55 | 31500 | 1.2674 |
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- | 1.5331 | 2.56 | 31600 | 1.2666 |
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- | 1.5685 | 2.56 | 31700 | 1.2673 |
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- | 1.5114 | 2.57 | 31800 | 1.2668 |
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- | 1.5574 | 2.58 | 31900 | 1.2675 |
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- | 1.527 | 2.59 | 32000 | 1.2678 |
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- | 1.5424 | 2.6 | 32100 | 1.2666 |
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- | 1.5706 | 2.6 | 32200 | 1.2676 |
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- | 1.5407 | 2.61 | 32300 | 1.2667 |
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- | 1.5534 | 2.62 | 32400 | 1.2677 |
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- | 1.5691 | 2.63 | 32500 | 1.2669 |
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- | 1.5223 | 2.64 | 32600 | 1.2662 |
375
- | 1.4817 | 2.64 | 32700 | 1.2642 |
376
- | 1.5603 | 2.65 | 32800 | 1.2652 |
377
- | 1.5072 | 2.66 | 32900 | 1.2641 |
378
- | 1.5691 | 2.67 | 33000 | 1.2633 |
379
- | 1.5673 | 2.68 | 33100 | 1.2643 |
380
- | 1.5433 | 2.69 | 33200 | 1.2645 |
381
- | 1.5102 | 2.69 | 33300 | 1.2634 |
382
- | 1.497 | 2.7 | 33400 | 1.2639 |
383
- | 1.564 | 2.71 | 33500 | 1.2636 |
384
- | 1.5131 | 2.72 | 33600 | 1.2637 |
385
- | 1.5138 | 2.73 | 33700 | 1.2632 |
386
- | 1.5345 | 2.73 | 33800 | 1.2634 |
387
- | 1.5539 | 2.74 | 33900 | 1.2631 |
388
- | 1.5571 | 2.75 | 34000 | 1.2634 |
389
- | 1.5325 | 2.76 | 34100 | 1.2634 |
390
- | 1.5434 | 2.77 | 34200 | 1.2635 |
391
- | 1.5053 | 2.77 | 34300 | 1.2630 |
392
- | 1.5503 | 2.78 | 34400 | 1.2633 |
393
- | 1.5414 | 2.79 | 34500 | 1.2632 |
394
- | 1.4909 | 2.8 | 34600 | 1.2627 |
395
- | 1.5447 | 2.81 | 34700 | 1.2626 |
396
- | 1.5897 | 2.81 | 34800 | 1.2630 |
397
- | 1.5738 | 2.82 | 34900 | 1.2634 |
398
- | 1.5125 | 2.83 | 35000 | 1.2632 |
399
- | 1.5532 | 2.84 | 35100 | 1.2633 |
400
- | 1.5423 | 2.85 | 35200 | 1.2632 |
401
- | 1.4817 | 2.86 | 35300 | 1.2631 |
402
- | 1.542 | 2.86 | 35400 | 1.2631 |
403
- | 1.5232 | 2.87 | 35500 | 1.2631 |
404
- | 1.556 | 2.88 | 35600 | 1.2631 |
405
- | 1.5154 | 2.89 | 35700 | 1.2631 |
406
- | 1.5174 | 2.9 | 35800 | 1.2631 |
407
- | 1.5469 | 2.9 | 35900 | 1.2631 |
408
- | 1.5171 | 2.91 | 36000 | 1.2630 |
409
 
410
 
411
  ### Framework versions
 
15
 
16
  This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 1.5295
19
 
20
  ## Model description
21
 
 
39
  - eval_batch_size: 4
40
  - seed: 42
41
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
42
+ - lr_scheduler_type: linear
43
+ - training_steps: 6000
44
 
45
  ### Training results
46
 
47
+ | Training Loss | Epoch | Step | Validation Loss |
48
+ |:-------------:|:-----:|:----:|:---------------:|
49
+ | 2.3757 | 0.04 | 100 | 2.0747 |
50
+ | 2.0269 | 0.08 | 200 | 1.9990 |
51
+ | 1.9535 | 0.12 | 300 | 1.9450 |
52
+ | 1.9136 | 0.16 | 400 | 1.9067 |
53
+ | 1.892 | 0.2 | 500 | 1.8757 |
54
+ | 1.8753 | 0.24 | 600 | 1.8574 |
55
+ | 1.8507 | 0.28 | 700 | 1.8359 |
56
+ | 1.8759 | 0.32 | 800 | 1.8167 |
57
+ | 1.8166 | 0.36 | 900 | 1.8054 |
58
+ | 1.8224 | 0.4 | 1000 | 1.7818 |
59
+ | 1.7852 | 0.44 | 1100 | 1.7814 |
60
+ | 1.8164 | 0.48 | 1200 | 1.7664 |
61
+ | 1.7632 | 0.52 | 1300 | 1.7598 |
62
+ | 1.8485 | 0.56 | 1400 | 1.7439 |
63
+ | 1.7712 | 0.6 | 1500 | 1.7303 |
64
+ | 1.7632 | 0.64 | 1600 | 1.7277 |
65
+ | 1.7378 | 0.68 | 1700 | 1.7135 |
66
+ | 1.7581 | 0.72 | 1800 | 1.7075 |
67
+ | 1.7261 | 0.76 | 1900 | 1.6933 |
68
+ | 1.7243 | 0.8 | 2000 | 1.6891 |
69
+ | 1.7311 | 0.84 | 2100 | 1.6837 |
70
+ | 1.7554 | 0.88 | 2200 | 1.6808 |
71
+ | 1.7026 | 0.92 | 2300 | 1.6646 |
72
+ | 1.7193 | 0.96 | 2400 | 1.6664 |
73
+ | 1.6861 | 1.0 | 2500 | 1.6577 |
74
+ | 1.68 | 1.04 | 2600 | 1.6470 |
75
+ | 1.5931 | 1.08 | 2700 | 1.6425 |
76
+ | 1.6655 | 1.12 | 2800 | 1.6352 |
77
+ | 1.629 | 1.16 | 2900 | 1.6298 |
78
+ | 1.6567 | 1.2 | 3000 | 1.6236 |
79
+ | 1.6225 | 1.24 | 3100 | 1.6242 |
80
+ | 1.6249 | 1.28 | 3200 | 1.6150 |
81
+ | 1.6263 | 1.32 | 3300 | 1.6077 |
82
+ | 1.6055 | 1.36 | 3400 | 1.6034 |
83
+ | 1.6338 | 1.4 | 3500 | 1.5996 |
84
+ | 1.6032 | 1.44 | 3600 | 1.5947 |
85
+ | 1.6447 | 1.48 | 3700 | 1.5882 |
86
+ | 1.6063 | 1.52 | 3800 | 1.5877 |
87
+ | 1.5933 | 1.56 | 3900 | 1.5850 |
88
+ | 1.6267 | 1.6 | 4000 | 1.5814 |
89
+ | 1.6151 | 1.64 | 4100 | 1.5709 |
90
+ | 1.6047 | 1.68 | 4200 | 1.5683 |
91
+ | 1.5811 | 1.72 | 4300 | 1.5661 |
92
+ | 1.5877 | 1.76 | 4400 | 1.5648 |
93
+ | 1.6321 | 1.8 | 4500 | 1.5645 |
94
+ | 1.5969 | 1.84 | 4600 | 1.5584 |
95
+ | 1.5971 | 1.88 | 4700 | 1.5565 |
96
+ | 1.622 | 1.92 | 4800 | 1.5547 |
97
+ | 1.6265 | 1.96 | 4900 | 1.5496 |
98
+ | 1.6145 | 2.0 | 5000 | 1.5466 |
99
+ | 1.526 | 2.04 | 5100 | 1.5427 |
100
+ | 1.5793 | 2.08 | 5200 | 1.5390 |
101
+ | 1.5714 | 2.12 | 5300 | 1.5375 |
102
+ | 1.5228 | 2.16 | 5400 | 1.5360 |
103
+ | 1.5383 | 2.2 | 5500 | 1.5343 |
104
+ | 1.5117 | 2.24 | 5600 | 1.5322 |
105
+ | 1.5427 | 2.28 | 5700 | 1.5316 |
106
+ | 1.4959 | 2.32 | 5800 | 1.5306 |
107
+ | 1.5456 | 2.36 | 5900 | 1.5299 |
108
+ | 1.5175 | 2.4 | 6000 | 1.5295 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
109
 
110
 
111
  ### Framework versions