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jvalline
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randomization_model

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  1. README.md +16 -16
  2. model.safetensors +1 -1
README.md CHANGED
@@ -18,11 +18,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.6576
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  - Bleu: 0.0001
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- - Wer: 0.9576
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- - Rougel: 0.119
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- - Gen Len: 18.9986
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  ## Model description
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@@ -54,18 +54,18 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Bleu | Wer | Rougel | Gen Len |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:-------:|
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- | 2.5767 | 0.16 | 1000 | 1.6626 | 0.0001 | 0.954 | 0.1251 | 18.9985 |
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- | 1.9533 | 0.32 | 2000 | 1.5147 | 0.0001 | 0.9524 | 0.1284 | 18.9986 |
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- | 1.8318 | 0.48 | 3000 | 1.4392 | 0.0001 | 0.9518 | 0.1298 | 18.9986 |
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- | 1.7626 | 0.64 | 4000 | 1.3857 | 0.0001 | 0.9514 | 0.1306 | 18.9986 |
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- | 1.7199 | 0.8 | 5000 | 1.3553 | 0.0001 | 0.951 | 0.1312 | 18.9988 |
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- | 1.6727 | 0.96 | 6000 | 1.3325 | 0.0001 | 0.9507 | 0.132 | 18.9986 |
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- | 1.9628 | 1.12 | 7000 | 1.8528 | 0.0001 | 0.9524 | 0.1293 | 18.9988 |
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- | 2.9138 | 1.28 | 8000 | 2.6299 | 0.0001 | 0.9568 | 0.1205 | 18.9986 |
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- | 3.5506 | 1.44 | 9000 | 2.7483 | 0.0001 | 0.958 | 0.1181 | 18.9987 |
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- | 3.5214 | 1.6 | 10000 | 2.7007 | 0.0001 | 0.9578 | 0.1186 | 18.9986 |
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- | 3.4669 | 1.76 | 11000 | 2.6699 | 0.0001 | 0.9576 | 0.1189 | 18.9986 |
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- | 3.4448 | 1.92 | 12000 | 2.6576 | 0.0001 | 0.9576 | 0.119 | 18.9986 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2766
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  - Bleu: 0.0001
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+ - Wer: 0.9507
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+ - Rougel: 0.1324
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+ - Gen Len: 18.9988
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Bleu | Wer | Rougel | Gen Len |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:-------:|
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+ | 1.6112 | 0.16 | 1000 | 1.2949 | 0.0001 | 0.9506 | 0.1323 | 18.9988 |
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+ | 1.5684 | 0.32 | 2000 | 1.2655 | 0.0001 | 0.9503 | 0.1328 | 18.9988 |
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+ | 1.5378 | 0.48 | 3000 | 1.2461 | 0.0001 | 0.95 | 0.1334 | 18.9985 |
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+ | 1.5183 | 0.64 | 4000 | 1.2246 | 0.0001 | 0.95 | 0.1334 | 18.9985 |
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+ | 1.5065 | 0.8 | 5000 | 1.2108 | 0.0001 | 0.9499 | 0.1336 | 18.9985 |
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+ | 1.4787 | 0.96 | 6000 | 1.1975 | 0.0001 | 0.9498 | 0.1338 | 18.9986 |
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+ | 1.5081 | 1.12 | 7000 | 1.2173 | 0.0001 | 0.9498 | 0.134 | 18.9986 |
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+ | 1.6302 | 1.28 | 8000 | 1.2801 | 0.0001 | 0.9507 | 0.1325 | 18.9988 |
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+ | 1.6337 | 1.44 | 9000 | 1.2766 | 0.0001 | 0.9507 | 0.1324 | 18.9988 |
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+ | 1.6333 | 1.6 | 10000 | 1.2766 | 0.0001 | 0.9507 | 0.1324 | 18.9988 |
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+ | 1.6246 | 1.76 | 11000 | 1.2766 | 0.0001 | 0.9507 | 0.1324 | 18.9988 |
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+ | 1.6219 | 1.92 | 12000 | 1.2766 | 0.0001 | 0.9507 | 0.1324 | 18.9988 |
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  ### Framework versions
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