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update model card README.md

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@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on the MAMI dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6324
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- - F1: 0.7900
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  ## Model description
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@@ -41,7 +41,7 @@ The following hyperparameters were used during training:
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  - eval_batch_size: 16
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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: reduce_lr_on_plateau
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  - lr_scheduler_warmup_ratio: 0.1
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  - num_epochs: 10.0
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@@ -49,13 +49,13 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | 0.6261 | 0.48 | 100 | 0.5528 | 0.7149 |
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- | 0.4739 | 0.96 | 200 | 0.4688 | 0.7795 |
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- | 0.3717 | 1.44 | 300 | 0.4479 | 0.7948 |
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- | 0.3705 | 1.91 | 400 | 0.4385 | 0.7924 |
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- | 0.253 | 2.39 | 500 | 0.5210 | 0.7956 |
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- | 0.2095 | 2.87 | 600 | 0.5216 | 0.7965 |
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- | 0.1371 | 3.35 | 700 | 0.6324 | 0.7900 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on the MAMI dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5796
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+ - F1: 0.7899
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  ## Model description
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  - eval_batch_size: 16
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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: linear
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  - lr_scheduler_warmup_ratio: 0.1
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  - num_epochs: 10.0
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 0.6898 | 0.48 | 100 | 0.6631 | 0.6076 |
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+ | 0.5824 | 0.96 | 200 | 0.5055 | 0.7545 |
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+ | 0.4306 | 1.44 | 300 | 0.4586 | 0.7861 |
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+ | 0.4207 | 1.91 | 400 | 0.4439 | 0.7927 |
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+ | 0.3055 | 2.39 | 500 | 0.4912 | 0.7949 |
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+ | 0.2582 | 2.87 | 600 | 0.4921 | 0.7873 |
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+ | 0.1875 | 3.35 | 700 | 0.5796 | 0.7899 |
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  ### Framework versions