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rotating-head-gp-gpt2-medium-wikitext

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Safetensors
rotating-head-gpt2
Generated from Trainer
custom_code
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  1. README.md +21 -21
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@@ -17,10 +17,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 3.2064
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- - Accuracy: 0.4186
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- - Perplexity: 24.6904
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- - Bleu: 0.1335
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  ## Model description
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@@ -52,23 +52,23 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Perplexity | Bleu |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|
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- | 5.9051 | 0.2806 | 500 | 5.7418 | 0.2238 | 311.6215 | 0.0481 |
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- | 4.8617 | 0.5612 | 1000 | 4.7405 | 0.2812 | 114.4922 | 0.0721 |
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- | 4.2992 | 0.8418 | 1500 | 4.2277 | 0.3179 | 68.5611 | 0.0832 |
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- | 3.9585 | 1.1223 | 2000 | 3.9252 | 0.3466 | 50.6640 | 0.0905 |
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- | 3.7838 | 1.4029 | 2500 | 3.7564 | 0.3626 | 42.7959 | 0.0986 |
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- | 3.6863 | 1.6835 | 3000 | 3.6388 | 0.3739 | 38.0459 | 0.1069 |
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- | 3.5869 | 1.9641 | 3500 | 3.5518 | 0.3826 | 34.8757 | 0.1100 |
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- | 3.4733 | 2.2447 | 4000 | 3.4846 | 0.3886 | 32.6092 | 0.1159 |
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- | 3.4122 | 2.5253 | 4500 | 3.4307 | 0.3941 | 30.8979 | 0.1212 |
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- | 3.3791 | 2.8058 | 5000 | 3.3804 | 0.3991 | 29.3811 | 0.1223 |
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- | 3.2616 | 3.0864 | 5500 | 3.3447 | 0.4026 | 28.3518 | 0.1222 |
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- | 3.2499 | 3.3670 | 6000 | 3.3096 | 0.4067 | 27.3740 | 0.1261 |
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- | 3.2277 | 3.6476 | 6500 | 3.2812 | 0.4100 | 26.6073 | 0.1299 |
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- | 3.1992 | 3.9282 | 7000 | 3.2523 | 0.4128 | 25.8505 | 0.1305 |
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- | 3.13 | 4.2088 | 7500 | 3.2332 | 0.4154 | 25.3608 | 0.1326 |
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- | 3.0915 | 4.4893 | 8000 | 3.2200 | 0.4168 | 25.0291 | 0.1317 |
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- | 3.1011 | 4.7699 | 8500 | 3.2064 | 0.4186 | 24.6904 | 0.1335 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 3.1985
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+ - Accuracy: 0.4196
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+ - Perplexity: 24.4954
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+ - Bleu: 0.1339
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Perplexity | Bleu |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|
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+ | 5.9062 | 0.2806 | 500 | 5.7470 | 0.2234 | 313.2463 | 0.0493 |
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+ | 4.8598 | 0.5612 | 1000 | 4.7428 | 0.2811 | 114.7554 | 0.0698 |
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+ | 4.3025 | 0.8418 | 1500 | 4.2329 | 0.3170 | 68.9191 | 0.0834 |
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+ | 3.9635 | 1.1223 | 2000 | 3.9291 | 0.3454 | 50.8590 | 0.0932 |
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+ | 3.7769 | 1.4029 | 2500 | 3.7427 | 0.3636 | 42.2098 | 0.1020 |
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+ | 3.6738 | 1.6835 | 3000 | 3.6225 | 0.3754 | 37.4295 | 0.1066 |
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+ | 3.5744 | 1.9641 | 3500 | 3.5325 | 0.3845 | 34.2102 | 0.1118 |
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+ | 3.456 | 2.2447 | 4000 | 3.4704 | 0.3902 | 32.1497 | 0.1139 |
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+ | 3.3972 | 2.5253 | 4500 | 3.4190 | 0.3955 | 30.5384 | 0.1230 |
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+ | 3.3654 | 2.8058 | 5000 | 3.3686 | 0.4007 | 29.0392 | 0.1230 |
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+ | 3.247 | 3.0864 | 5500 | 3.3328 | 0.4043 | 28.0168 | 0.1247 |
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+ | 3.2403 | 3.3670 | 6000 | 3.2985 | 0.4083 | 27.0714 | 0.1298 |
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+ | 3.2167 | 3.6476 | 6500 | 3.2693 | 0.4112 | 26.2922 | 0.1288 |
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+ | 3.1903 | 3.9282 | 7000 | 3.2456 | 0.4134 | 25.6768 | 0.1305 |
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+ | 3.1212 | 4.2088 | 7500 | 3.2262 | 0.4161 | 25.1831 | 0.1325 |
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+ | 3.0816 | 4.4893 | 8000 | 3.2128 | 0.4176 | 24.8480 | 0.1307 |
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+ | 3.0917 | 4.7699 | 8500 | 3.1985 | 0.4196 | 24.4954 | 0.1339 |
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  ### Framework versions