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# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-05-17_gofor50

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4441
- Accuracy: 0.7
- Exit 0 Accuracy: 0.15
- Exit 1 Accuracy: 0.1775
- Exit 2 Accuracy: 0.515
- Exit 3 Accuracy: 0.705
- Exit 4 Accuracy: 0.7025

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
| No log        | 0.96  | 12   | 2.6817          | 0.1475   | 0.0825          | 0.0525          | 0.1225          | 0.0625          | 0.0625          |
| No log        | 2.0   | 25   | 2.5448          | 0.235    | 0.0875          | 0.1125          | 0.0875          | 0.12            | 0.0625          |
| No log        | 2.96  | 37   | 2.3948          | 0.2825   | 0.105           | 0.14            | 0.205           | 0.21            | 0.0625          |
| No log        | 4.0   | 50   | 2.4502          | 0.3125   | 0.1125          | 0.1425          | 0.2             | 0.1725          | 0.22            |
| No log        | 4.96  | 62   | 2.1482          | 0.385    | 0.11            | 0.145           | 0.2925          | 0.3275          | 0.275           |
| No log        | 6.0   | 75   | 1.8840          | 0.465    | 0.1175          | 0.1475          | 0.33            | 0.4075          | 0.4925          |
| No log        | 6.96  | 87   | 1.6813          | 0.5725   | 0.1225          | 0.16            | 0.3475          | 0.4475          | 0.55            |
| No log        | 8.0   | 100  | 1.5199          | 0.605    | 0.13            | 0.1575          | 0.3375          | 0.5             | 0.585           |
| No log        | 8.96  | 112  | 1.4031          | 0.6125   | 0.1325          | 0.1575          | 0.365           | 0.5325          | 0.6025          |
| No log        | 10.0  | 125  | 1.3312          | 0.62     | 0.13            | 0.1575          | 0.3825          | 0.565           | 0.62            |
| No log        | 10.96 | 137  | 1.2133          | 0.6575   | 0.1175          | 0.1575          | 0.385           | 0.605           | 0.6275          |
| No log        | 12.0  | 150  | 1.1805          | 0.68     | 0.135           | 0.1675          | 0.38            | 0.6225          | 0.645           |
| No log        | 12.96 | 162  | 1.1987          | 0.6625   | 0.12            | 0.165           | 0.4075          | 0.63            | 0.645           |
| No log        | 14.0  | 175  | 1.1304          | 0.6975   | 0.12            | 0.17            | 0.41            | 0.6375          | 0.6725          |
| No log        | 14.96 | 187  | 1.1333          | 0.695    | 0.125           | 0.1675          | 0.42            | 0.655           | 0.675           |
| No log        | 16.0  | 200  | 1.1373          | 0.6975   | 0.1425          | 0.1725          | 0.4375          | 0.655           | 0.6825          |
| No log        | 16.96 | 212  | 1.0988          | 0.7125   | 0.1475          | 0.165           | 0.44            | 0.66            | 0.6975          |
| No log        | 18.0  | 225  | 1.1830          | 0.7      | 0.125           | 0.165           | 0.455           | 0.675           | 0.69            |
| No log        | 18.96 | 237  | 1.1827          | 0.685    | 0.13            | 0.1575          | 0.45            | 0.665           | 0.6825          |
| No log        | 20.0  | 250  | 1.2335          | 0.695    | 0.125           | 0.1575          | 0.455           | 0.6675          | 0.685           |
| No log        | 20.96 | 262  | 1.2793          | 0.6625   | 0.135           | 0.1575          | 0.4475          | 0.665           | 0.6775          |
| No log        | 22.0  | 275  | 1.2136          | 0.71     | 0.14            | 0.155           | 0.4675          | 0.6775          | 0.6925          |
| No log        | 22.96 | 287  | 1.1790          | 0.7025   | 0.135           | 0.16            | 0.4725          | 0.6725          | 0.6975          |
| No log        | 24.0  | 300  | 1.2636          | 0.6825   | 0.125           | 0.1625          | 0.4375          | 0.6675          | 0.6825          |
| No log        | 24.96 | 312  | 1.2949          | 0.6775   | 0.1225          | 0.165           | 0.46            | 0.6625          | 0.685           |
| No log        | 26.0  | 325  | 1.2979          | 0.685    | 0.135           | 0.1625          | 0.455           | 0.69            | 0.6925          |
| No log        | 26.96 | 337  | 1.3059          | 0.6825   | 0.1375          | 0.1675          | 0.475           | 0.675           | 0.69            |
| No log        | 28.0  | 350  | 1.3239          | 0.6975   | 0.1375          | 0.17            | 0.4725          | 0.68            | 0.705           |
| No log        | 28.96 | 362  | 1.3181          | 0.7      | 0.1325          | 0.1725          | 0.465           | 0.6725          | 0.695           |
| No log        | 30.0  | 375  | 1.3978          | 0.68     | 0.1375          | 0.1675          | 0.4825          | 0.6825          | 0.68            |
| No log        | 30.96 | 387  | 1.2876          | 0.715    | 0.14            | 0.175           | 0.49            | 0.6925          | 0.7125          |
| No log        | 32.0  | 400  | 1.3233          | 0.6975   | 0.1475          | 0.1725          | 0.4875          | 0.6975          | 0.705           |
| No log        | 32.96 | 412  | 1.3607          | 0.7      | 0.135           | 0.1725          | 0.4925          | 0.6925          | 0.7075          |
| No log        | 34.0  | 425  | 1.3728          | 0.6925   | 0.1525          | 0.1725          | 0.4925          | 0.685           | 0.7025          |
| No log        | 34.96 | 437  | 1.3931          | 0.695    | 0.1425          | 0.1725          | 0.49            | 0.695           | 0.7             |
| No log        | 36.0  | 450  | 1.3642          | 0.7125   | 0.145           | 0.1725          | 0.505           | 0.6875          | 0.71            |
| No log        | 36.96 | 462  | 1.3807          | 0.705    | 0.1475          | 0.17            | 0.4975          | 0.6875          | 0.695           |
| No log        | 38.0  | 475  | 1.4171          | 0.7025   | 0.1575          | 0.17            | 0.505           | 0.6975          | 0.695           |
| No log        | 38.96 | 487  | 1.3868          | 0.71     | 0.155           | 0.17            | 0.4975          | 0.7025          | 0.705           |
| 0.6814        | 40.0  | 500  | 1.3982          | 0.7075   | 0.1425          | 0.17            | 0.51            | 0.6975          | 0.705           |
| 0.6814        | 40.96 | 512  | 1.3898          | 0.7075   | 0.1475          | 0.17            | 0.51            | 0.6975          | 0.6975          |
| 0.6814        | 42.0  | 525  | 1.4277          | 0.7025   | 0.145           | 0.17            | 0.515           | 0.705           | 0.705           |
| 0.6814        | 42.96 | 537  | 1.4381          | 0.7025   | 0.145           | 0.1725          | 0.51            | 0.7             | 0.7025          |
| 0.6814        | 44.0  | 550  | 1.4374          | 0.7025   | 0.15            | 0.175           | 0.5125          | 0.7             | 0.7             |
| 0.6814        | 44.96 | 562  | 1.4260          | 0.6975   | 0.1475          | 0.175           | 0.5125          | 0.7025          | 0.7             |
| 0.6814        | 46.0  | 575  | 1.4408          | 0.7      | 0.1475          | 0.1775          | 0.5125          | 0.7025          | 0.7             |
| 0.6814        | 46.96 | 587  | 1.4483          | 0.7      | 0.15            | 0.1775          | 0.515           | 0.705           | 0.7             |
| 0.6814        | 48.0  | 600  | 1.4441          | 0.7      | 0.15            | 0.1775          | 0.515           | 0.705           | 0.7025          |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.12.0
- Tokenizers 0.12.1

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-05-17_gofor50", "results": []}]}
transformers pytorch tensorboard layoutlmv3 text-classification generated_from_trainer license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #text-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

EElayoutlmv3\_jordyvl\_rvl\_cdip\_100\_examples\_per\_class\_2023-05-17\_gofor50
================================================================================


This model is a fine-tuned version of microsoft/layoutlmv3-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4441
* Accuracy: 0.7
* Exit 0 Accuracy: 0.15
* Exit 1 Accuracy: 0.1775
* Exit 2 Accuracy: 0.515
* Exit 3 Accuracy: 0.705
* Exit 4 Accuracy: 0.7025


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.12.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.12.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #text-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.12.0
* Tokenizers 0.12.1