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layoutlmv2-large-uncased-finetuned-vi-infovqa

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@@ -14,7 +14,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/layoutlmv2-large-uncased](https://huggingface.co/microsoft/layoutlmv2-large-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 6.2383
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  ## Model description
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@@ -33,35 +33,59 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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  - train_batch_size: 2
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  - eval_batch_size: 2
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  - seed: 250500
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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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- - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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- | 5.9983 | 0.83 | 500 | 6.2383 |
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- | 6.2621 | 1.65 | 1000 | 6.2383 |
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- | 6.2492 | 2.48 | 1500 | 6.2383 |
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- | 6.2548 | 3.3 | 2000 | 6.2383 |
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- | 6.2515 | 4.13 | 2500 | 6.2383 |
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- | 6.2543 | 4.95 | 3000 | 6.2383 |
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- | 6.2545 | 5.78 | 3500 | 6.2383 |
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- | 6.247 | 6.6 | 4000 | 6.2383 |
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- | 6.2499 | 7.43 | 4500 | 6.2383 |
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- | 6.2434 | 8.25 | 5000 | 6.2383 |
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- | 6.2416 | 9.08 | 5500 | 6.2383 |
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- | 6.2425 | 9.9 | 6000 | 6.2383 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.14.1
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  - Pytorch 1.8.0+cu101
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- - Datasets 1.16.1
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  - Tokenizers 0.10.3
 
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  This model is a fine-tuned version of [microsoft/layoutlmv2-large-uncased](https://huggingface.co/microsoft/layoutlmv2-large-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 8.5806
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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  - train_batch_size: 2
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  - eval_batch_size: 2
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  - seed: 250500
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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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+ - num_epochs: 6
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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+ | No log | 0.17 | 100 | 4.6181 |
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+ | No log | 0.33 | 200 | 4.3357 |
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+ | No log | 0.5 | 300 | 4.3897 |
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+ | No log | 0.66 | 400 | 4.8238 |
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+ | 4.4277 | 0.83 | 500 | 3.9088 |
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+ | 4.4277 | 0.99 | 600 | 3.6063 |
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+ | 4.4277 | 1.16 | 700 | 3.4278 |
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+ | 4.4277 | 1.32 | 800 | 3.5428 |
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+ | 4.4277 | 1.49 | 900 | 3.4331 |
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+ | 3.0413 | 1.65 | 1000 | 3.3699 |
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+ | 3.0413 | 1.82 | 1100 | 3.3622 |
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+ | 3.0413 | 1.98 | 1200 | 3.5294 |
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+ | 3.0413 | 2.15 | 1300 | 3.7918 |
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+ | 3.0413 | 2.31 | 1400 | 3.4007 |
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+ | 2.0843 | 2.48 | 1500 | 4.0296 |
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+ | 2.0843 | 2.64 | 1600 | 4.1852 |
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+ | 2.0843 | 2.81 | 1700 | 3.6690 |
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+ | 2.0843 | 2.97 | 1800 | 3.6089 |
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+ | 2.0843 | 3.14 | 1900 | 5.5534 |
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+ | 1.7527 | 3.3 | 2000 | 4.7498 |
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+ | 1.7527 | 3.47 | 2100 | 5.2691 |
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+ | 1.7527 | 3.63 | 2200 | 5.1324 |
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+ | 1.7527 | 3.8 | 2300 | 4.5912 |
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+ | 1.7527 | 3.96 | 2400 | 4.1727 |
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+ | 1.2037 | 4.13 | 2500 | 6.1174 |
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+ | 1.2037 | 4.29 | 2600 | 5.7172 |
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+ | 1.2037 | 4.46 | 2700 | 5.8843 |
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+ | 1.2037 | 4.62 | 2800 | 6.4232 |
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+ | 1.2037 | 4.79 | 2900 | 7.4486 |
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+ | 0.8386 | 4.95 | 3000 | 7.1946 |
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+ | 0.8386 | 5.12 | 3100 | 7.9869 |
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+ | 0.8386 | 5.28 | 3200 | 8.0310 |
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+ | 0.8386 | 5.45 | 3300 | 8.2954 |
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+ | 0.8386 | 5.61 | 3400 | 8.5361 |
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+ | 0.4389 | 5.78 | 3500 | 8.6040 |
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+ | 0.4389 | 5.94 | 3600 | 8.5806 |
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  ### Framework versions
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+ - Transformers 4.15.0
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  - Pytorch 1.8.0+cu101
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+ - Datasets 1.17.0
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  - Tokenizers 0.10.3