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# layoutlmv3-real_triplet

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: 0.0279
- Item: {'precision': 0.9425511197663097, 'recall': 0.7378048780487805, 'f1': 0.827704147071398, 'number': 2624}
- Aption: {'precision': 0.8349913494809689, 'recall': 0.7924876847290641, 'f1': 0.81318449873631, 'number': 4872}
- Ootnote: {'precision': 0.7846153846153846, 'recall': 0.8360655737704918, 'f1': 0.8095238095238095, 'number': 122}
- Ormula: {'precision': 0.9865976241242765, 'recall': 0.9920367534456356, 'f1': 0.9893097128894318, 'number': 3265}
- Overall Precision: 0.9056
- Overall Recall: 0.8397
- Overall F1: 0.8714
- Overall Accuracy: 0.9961

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Item                                                                                                       | Aption                                                                                                    | Ootnote                                                                                                  | Ormula                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0166        | 1.0   | 8507  | 0.0252          | {'precision': 0.9114948731786292, 'recall': 0.6436737804878049, 'f1': 0.7545231181594819, 'number': 2624}  | {'precision': 0.778075463273052, 'recall': 0.715311986863711, 'f1': 0.7453748262217944, 'number': 4872}   | {'precision': 0.8598130841121495, 'recall': 0.7540983606557377, 'f1': 0.8034934497816593, 'number': 122} | {'precision': 0.976365868631062, 'recall': 0.9742725880551302, 'f1': 0.9753181051663345, 'number': 3265}  | 0.8711            | 0.7762         | 0.8209     | 0.9948           |
| 0.0075        | 2.0   | 17014 | 0.0318          | {'precision': 0.9079837618403248, 'recall': 0.5114329268292683, 'f1': 0.6543149683081424, 'number': 2624}  | {'precision': 0.7194696441032798, 'recall': 0.6348522167487685, 'f1': 0.6745175008177952, 'number': 4872} | {'precision': 0.9207920792079208, 'recall': 0.7622950819672131, 'f1': 0.8340807174887892, 'number': 122} | {'precision': 0.9831132944427388, 'recall': 0.9807044410413476, 'f1': 0.9819073903710519, 'number': 3265} | 0.8462            | 0.7103         | 0.7723     | 0.9938           |
| 0.0057        | 3.0   | 25521 | 0.0338          | {'precision': 0.9227359088030399, 'recall': 0.5552591463414634, 'f1': 0.6933142993100166, 'number': 2624}  | {'precision': 0.7442236598890942, 'recall': 0.6611247947454844, 'f1': 0.7002173913043479, 'number': 4872} | {'precision': 0.8859649122807017, 'recall': 0.8278688524590164, 'f1': 0.8559322033898306, 'number': 122} | {'precision': 0.9791538933169834, 'recall': 0.9782542113323124, 'f1': 0.9787038455645779, 'number': 3265} | 0.8589            | 0.7326         | 0.7907     | 0.9942           |
| 0.004         | 4.0   | 34028 | 0.0615          | {'precision': 0.9321486268174475, 'recall': 0.43978658536585363, 'f1': 0.5976178146038322, 'number': 2624} | {'precision': 0.6900404088424055, 'recall': 0.5958538587848933, 'f1': 0.639497742042075, 'number': 4872}  | {'precision': 0.8738738738738738, 'recall': 0.7950819672131147, 'f1': 0.832618025751073, 'number': 122}  | {'precision': 0.9880660954712362, 'recall': 0.9889739663093415, 'f1': 0.9885198224399205, 'number': 3265} | 0.8367            | 0.6784         | 0.7493     | 0.9932           |
| 0.0027        | 5.0   | 42535 | 0.0227          | {'precision': 0.9356973995271868, 'recall': 0.7541920731707317, 'f1': 0.8351972990082295, 'number': 2624}  | {'precision': 0.843103448275862, 'recall': 0.8029556650246306, 'f1': 0.8225399495374264, 'number': 4872}  | {'precision': 0.8571428571428571, 'recall': 0.7868852459016393, 'f1': 0.8205128205128205, 'number': 122} | {'precision': 0.9850655288021944, 'recall': 0.9898928024502297, 'f1': 0.9874732661167125, 'number': 3265} | 0.9085            | 0.8471         | 0.8767     | 0.9963           |
| 0.0021        | 6.0   | 51042 | 0.0165          | {'precision': 0.9341987466427932, 'recall': 0.7953506097560976, 'f1': 0.859201317414574, 'number': 2624}   | {'precision': 0.856687898089172, 'recall': 0.8282019704433498, 'f1': 0.8422041327489042, 'number': 4872}  | {'precision': 0.9174311926605505, 'recall': 0.819672131147541, 'f1': 0.8658008658008659, 'number': 122}  | {'precision': 0.9736523319200484, 'recall': 0.9846860643185299, 'f1': 0.9791381148165067, 'number': 3265} | 0.9113            | 0.8671         | 0.8887     | 0.9966           |
| 0.0015        | 7.0   | 59549 | 0.0271          | {'precision': 0.9294605809128631, 'recall': 0.6829268292682927, 'f1': 0.7873462214411249, 'number': 2624}  | {'precision': 0.8111135515045025, 'recall': 0.7580049261083743, 'f1': 0.7836604774535808, 'number': 4872} | {'precision': 0.8389830508474576, 'recall': 0.8114754098360656, 'f1': 0.825, 'number': 122}              | {'precision': 0.9880879657910813, 'recall': 0.9908116385911179, 'f1': 0.9894479278177092, 'number': 3265} | 0.8932            | 0.8103         | 0.8498     | 0.9956           |
| 0.0012        | 8.0   | 68056 | 0.0231          | {'precision': 0.9250706880301602, 'recall': 0.7480945121951219, 'f1': 0.8272229245680573, 'number': 2624}  | {'precision': 0.8451156812339332, 'recall': 0.8097290640394089, 'f1': 0.8270440251572327, 'number': 4872} | {'precision': 0.8962264150943396, 'recall': 0.7786885245901639, 'f1': 0.8333333333333333, 'number': 122} | {'precision': 0.9889739663093415, 'recall': 0.9889739663093415, 'f1': 0.9889739663093415, 'number': 3265} | 0.9086            | 0.8483         | 0.8774     | 0.9962           |
| 0.0009        | 9.0   | 76563 | 0.0224          | {'precision': 0.9263715110683349, 'recall': 0.7336128048780488, 'f1': 0.8188005104210974, 'number': 2624}  | {'precision': 0.835820895522388, 'recall': 0.7931034482758621, 'f1': 0.8139020537124803, 'number': 4872}  | {'precision': 0.832, 'recall': 0.8524590163934426, 'f1': 0.8421052631578947, 'number': 122}              | {'precision': 0.9829787234042553, 'recall': 0.9905053598774886, 'f1': 0.9867276887871854, 'number': 3265} | 0.9022            | 0.8386         | 0.8693     | 0.9960           |
| 0.0007        | 10.0  | 85070 | 0.0279          | {'precision': 0.9425511197663097, 'recall': 0.7378048780487805, 'f1': 0.827704147071398, 'number': 2624}   | {'precision': 0.8349913494809689, 'recall': 0.7924876847290641, 'f1': 0.81318449873631, 'number': 4872}   | {'precision': 0.7846153846153846, 'recall': 0.8360655737704918, 'f1': 0.8095238095238095, 'number': 122} | {'precision': 0.9865976241242765, 'recall': 0.9920367534456356, 'f1': 0.9893097128894318, 'number': 3265} | 0.9056            | 0.8397         | 0.8714     | 0.9961           |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv3-real_triplet", "results": []}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

layoutlmv3-real\_triplet
========================


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: 0.0279
* Item: {'precision': 0.9425511197663097, 'recall': 0.7378048780487805, 'f1': 0.827704147071398, 'number': 2624}
* Aption: {'precision': 0.8349913494809689, 'recall': 0.7924876847290641, 'f1': 0.81318449873631, 'number': 4872}
* Ootnote: {'precision': 0.7846153846153846, 'recall': 0.8360655737704918, 'f1': 0.8095238095238095, 'number': 122}
* Ormula: {'precision': 0.9865976241242765, 'recall': 0.9920367534456356, 'f1': 0.9893097128894318, 'number': 3265}
* Overall Precision: 0.9056
* Overall Recall: 0.8397
* Overall F1: 0.8714
* Overall Accuracy: 0.9961


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: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-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: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-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: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.13.2