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# layoutlmv3-finetuned-cord_100

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2268
- Precision: 0.9386
- Recall: 0.9491
- F1: 0.9438
- Accuracy: 0.9542

## 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: 1e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.56  | 250  | 1.0455          | 0.7145    | 0.7867 | 0.7488 | 0.7886   |
| 1.4126        | 3.12  | 500  | 0.5824          | 0.8322    | 0.8615 | 0.8466 | 0.8697   |
| 1.4126        | 4.69  | 750  | 0.4120          | 0.8620    | 0.8975 | 0.8794 | 0.9053   |
| 0.4193        | 6.25  | 1000 | 0.3474          | 0.9020    | 0.9229 | 0.9123 | 0.9283   |
| 0.4193        | 7.81  | 1250 | 0.2864          | 0.9210    | 0.9341 | 0.9275 | 0.9406   |
| 0.2287        | 9.38  | 1500 | 0.2501          | 0.9290    | 0.9401 | 0.9345 | 0.9457   |
| 0.2287        | 10.94 | 1750 | 0.2451          | 0.9270    | 0.9409 | 0.9339 | 0.9469   |
| 0.1607        | 12.5  | 2000 | 0.2408          | 0.9348    | 0.9446 | 0.9397 | 0.9520   |
| 0.1607        | 14.06 | 2250 | 0.2296          | 0.9328    | 0.9461 | 0.9394 | 0.9537   |
| 0.1235        | 15.62 | 2500 | 0.2268          | 0.9386    | 0.9491 | 0.9438 | 0.9542   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["cord-layoutlmv3"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-cord_100", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "cord-layoutlmv3", "type": "cord-layoutlmv3", "config": "cord", "split": "test", "args": "cord"}, "metrics": [{"type": "precision", "value": 0.9385640266469282, "name": "Precision"}, {"type": "recall", "value": 0.9491017964071856, "name": "Recall"}, {"type": "f1", "value": 0.9438034983252697, "name": "F1"}, {"type": "accuracy", "value": 0.9541595925297114, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer dataset:cord-layoutlmv3 license:cc-by-nc-sa-4.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-cord-layoutlmv3 #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

layoutlmv3-finetuned-cord\_100
==============================


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


* Loss: 0.2268
* Precision: 0.9386
* Recall: 0.9491
* F1: 0.9438
* Accuracy: 0.9542


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: 1e-05
* train\_batch\_size: 5
* eval\_batch\_size: 5
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2500


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 5
* eval\_batch\_size: 5
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2500 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-cord-layoutlmv3 #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 5
* eval\_batch\_size: 5
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2500 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-cord-layoutlmv3 #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 5
* eval\_batch\_size: 5
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2500### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3