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gpt2-wikitext

Text Generation
Transformers
Safetensors
gpt2
Generated from Trainer
text-generation-inference
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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: 3.1672
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- - Accuracy: 0.4214
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- - Perplexity: 23.7410
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- - Bleu: 0.1474
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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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- | 6.0797 | 0.2806 | 500 | 5.9513 | 0.1886 | 384.2374 | 0.0328 |
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- | 5.0682 | 0.5612 | 1000 | 4.9243 | 0.2616 | 137.5896 | 0.0640 |
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- | 4.3376 | 0.8418 | 1500 | 4.2176 | 0.3220 | 67.8687 | 0.0837 |
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- | 3.946 | 1.1223 | 2000 | 3.8835 | 0.3531 | 48.5917 | 0.1062 |
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- | 3.7578 | 1.4029 | 2500 | 3.7072 | 0.3683 | 40.7404 | 0.1239 |
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- | 3.6484 | 1.6835 | 3000 | 3.5824 | 0.3790 | 35.9600 | 0.1312 |
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- | 3.5434 | 1.9641 | 3500 | 3.4942 | 0.3874 | 32.9255 | 0.1354 |
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- | 3.4228 | 2.2447 | 4000 | 3.4280 | 0.3938 | 30.8151 | 0.1332 |
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- | 3.3604 | 2.5253 | 4500 | 3.3732 | 0.3989 | 29.1719 | 0.1409 |
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- | 3.3288 | 2.8058 | 5000 | 3.3268 | 0.4039 | 27.8495 | 0.1396 |
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- | 3.2073 | 3.0864 | 5500 | 3.2888 | 0.4079 | 26.8096 | 0.1410 |
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- | 3.2009 | 3.3670 | 6000 | 3.2614 | 0.4109 | 26.0870 | 0.1426 |
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- | 3.1787 | 3.6476 | 6500 | 3.2330 | 0.4141 | 25.3543 | 0.1394 |
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- | 3.1528 | 3.9282 | 7000 | 3.2094 | 0.4164 | 24.7635 | 0.1459 |
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- | 3.0849 | 4.2088 | 7500 | 3.1927 | 0.4182 | 24.3547 | 0.1420 |
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- | 3.0471 | 4.4893 | 8000 | 3.1799 | 0.4200 | 24.0448 | 0.1476 |
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- | 3.0571 | 4.7699 | 8500 | 3.1672 | 0.4214 | 23.7410 | 0.1474 |
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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: 3.1673
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+ - Accuracy: 0.4215
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+ - Perplexity: 23.7437
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+ - Bleu: 0.1486
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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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+ | 6.0809 | 0.2806 | 500 | 5.9591 | 0.1879 | 387.2722 | 0.0323 |
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+ | 5.0659 | 0.5612 | 1000 | 4.9189 | 0.2618 | 136.8551 | 0.0639 |
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+ | 4.338 | 0.8418 | 1500 | 4.2174 | 0.3219 | 67.8594 | 0.0857 |
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+ | 3.9468 | 1.1223 | 2000 | 3.8832 | 0.3532 | 48.5783 | 0.1094 |
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+ | 3.7577 | 1.4029 | 2500 | 3.7060 | 0.3685 | 40.6890 | 0.1226 |
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+ | 3.6483 | 1.6835 | 3000 | 3.5831 | 0.3787 | 35.9858 | 0.1296 |
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+ | 3.5432 | 1.9641 | 3500 | 3.4948 | 0.3875 | 32.9448 | 0.1360 |
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+ | 3.4221 | 2.2447 | 4000 | 3.4280 | 0.3939 | 30.8160 | 0.1306 |
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+ | 3.3602 | 2.5253 | 4500 | 3.3724 | 0.3991 | 29.1478 | 0.1391 |
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+ | 3.3285 | 2.8058 | 5000 | 3.3261 | 0.4038 | 27.8284 | 0.1369 |
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+ | 3.2072 | 3.0864 | 5500 | 3.2882 | 0.4077 | 26.7936 | 0.1447 |
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+ | 3.2002 | 3.3670 | 6000 | 3.2611 | 0.4112 | 26.0792 | 0.1472 |
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+ | 3.1782 | 3.6476 | 6500 | 3.2317 | 0.4138 | 25.3223 | 0.1421 |
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+ | 3.153 | 3.9282 | 7000 | 3.2080 | 0.4164 | 24.7294 | 0.1415 |
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+ | 3.0846 | 4.2088 | 7500 | 3.1915 | 0.4185 | 24.3249 | 0.1470 |
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+ | 3.0469 | 4.4893 | 8000 | 3.1789 | 0.4199 | 24.0205 | 0.1444 |
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+ | 3.0567 | 4.7699 | 8500 | 3.1673 | 0.4215 | 23.7437 | 0.1486 |
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  ### Framework versions