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# layoutmlv2_funsd_rjz

This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9422
- Answer: {'precision': 0.7382857142857143, 'recall': 0.7985166872682324, 'f1': 0.7672209026128266, 'number': 809}
- Header: {'precision': 0.42758620689655175, 'recall': 0.5210084033613446, 'f1': 0.4696969696969697, 'number': 119}
- Question: {'precision': 0.8075160403299725, 'recall': 0.8272300469483568, 'f1': 0.8172541743970314, 'number': 1065}
- Overall Precision: 0.7527
- Overall Recall: 0.7973
- Overall F1: 0.7744
- Overall Accuracy: 0.8096

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                      | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3143        | 1.0   | 10   | 0.7685          | {'precision': 0.7, 'recall': 0.7700865265760197, 'f1': 0.7333725721012359, 'number': 809}                | {'precision': 0.2986111111111111, 'recall': 0.36134453781512604, 'f1': 0.32699619771863114, 'number': 119}  | {'precision': 0.7693032015065914, 'recall': 0.7671361502347418, 'f1': 0.768218147625764, 'number': 1065}  | 0.7075            | 0.7441         | 0.7254     | 0.7924           |
| 0.2816        | 2.0   | 20   | 0.7829          | {'precision': 0.7162315550510783, 'recall': 0.7799752781211372, 'f1': 0.7467455621301775, 'number': 809} | {'precision': 0.33152173913043476, 'recall': 0.5126050420168067, 'f1': 0.40264026402640263, 'number': 119}  | {'precision': 0.7855839416058394, 'recall': 0.8084507042253521, 'f1': 0.7968533086534013, 'number': 1065} | 0.7186            | 0.7792         | 0.7477     | 0.7976           |
| 0.2216        | 3.0   | 30   | 0.7825          | {'precision': 0.7016806722689075, 'recall': 0.8257107540173053, 'f1': 0.7586598523566157, 'number': 809} | {'precision': 0.35570469798657717, 'recall': 0.44537815126050423, 'f1': 0.39552238805970147, 'number': 119} | {'precision': 0.7851985559566786, 'recall': 0.8169014084507042, 'f1': 0.8007363092498849, 'number': 1065} | 0.7202            | 0.7983         | 0.7573     | 0.7942           |
| 0.1973        | 4.0   | 40   | 0.7683          | {'precision': 0.7095032397408207, 'recall': 0.8121137206427689, 'f1': 0.7573487031700288, 'number': 809} | {'precision': 0.3968253968253968, 'recall': 0.42016806722689076, 'f1': 0.40816326530612246, 'number': 119}  | {'precision': 0.802367941712204, 'recall': 0.8272300469483568, 'f1': 0.8146093388811835, 'number': 1065}  | 0.7386            | 0.7968         | 0.7666     | 0.8143           |
| 0.1671        | 5.0   | 50   | 0.7918          | {'precision': 0.7269585253456221, 'recall': 0.7799752781211372, 'f1': 0.7525342874180083, 'number': 809} | {'precision': 0.4076923076923077, 'recall': 0.44537815126050423, 'f1': 0.42570281124497994, 'number': 119}  | {'precision': 0.7848888888888889, 'recall': 0.8291079812206573, 'f1': 0.8063926940639269, 'number': 1065} | 0.7381            | 0.7863         | 0.7614     | 0.8139           |
| 0.1342        | 6.0   | 60   | 0.8295          | {'precision': 0.7234972677595628, 'recall': 0.8182941903584673, 'f1': 0.7679814385150812, 'number': 809} | {'precision': 0.37857142857142856, 'recall': 0.44537815126050423, 'f1': 0.4092664092664093, 'number': 119}  | {'precision': 0.7939339875111507, 'recall': 0.8356807511737089, 'f1': 0.8142726440988106, 'number': 1065} | 0.7376            | 0.8053         | 0.7700     | 0.8120           |
| 0.1212        | 7.0   | 70   | 0.8632          | {'precision': 0.7337883959044369, 'recall': 0.7972805933250927, 'f1': 0.764218009478673, 'number': 809}  | {'precision': 0.4084507042253521, 'recall': 0.48739495798319327, 'f1': 0.4444444444444445, 'number': 119}   | {'precision': 0.8137347130761995, 'recall': 0.812206572769953, 'f1': 0.8129699248120301, 'number': 1065}  | 0.7524            | 0.7868         | 0.7692     | 0.8082           |
| 0.1131        | 8.0   | 80   | 0.9081          | {'precision': 0.7244785949506037, 'recall': 0.8158220024721878, 'f1': 0.7674418604651163, 'number': 809} | {'precision': 0.40131578947368424, 'recall': 0.5126050420168067, 'f1': 0.4501845018450184, 'number': 119}   | {'precision': 0.8097876269621422, 'recall': 0.8234741784037559, 'f1': 0.8165735567970206, 'number': 1065} | 0.7446            | 0.8018         | 0.7722     | 0.8011           |
| 0.1043        | 9.0   | 90   | 0.9021          | {'precision': 0.7308132875143184, 'recall': 0.788627935723115, 'f1': 0.7586206896551724, 'number': 809}  | {'precision': 0.425531914893617, 'recall': 0.5042016806722689, 'f1': 0.4615384615384615, 'number': 119}     | {'precision': 0.7914818101153505, 'recall': 0.8375586854460094, 'f1': 0.8138686131386863, 'number': 1065} | 0.7426            | 0.7978         | 0.7692     | 0.8075           |
| 0.0884        | 10.0  | 100  | 0.9126          | {'precision': 0.7231450719822813, 'recall': 0.8071693448702101, 'f1': 0.7628504672897196, 'number': 809} | {'precision': 0.40939597315436244, 'recall': 0.5126050420168067, 'f1': 0.4552238805970149, 'number': 119}   | {'precision': 0.819718309859155, 'recall': 0.819718309859155, 'f1': 0.819718309859155, 'number': 1065}    | 0.7496            | 0.7963         | 0.7723     | 0.8094           |
| 0.084         | 11.0  | 110  | 0.9354          | {'precision': 0.7502944640753828, 'recall': 0.7873918417799752, 'f1': 0.7683956574185766, 'number': 809} | {'precision': 0.4140127388535032, 'recall': 0.5462184873949579, 'f1': 0.47101449275362317, 'number': 119}   | {'precision': 0.7946428571428571, 'recall': 0.8356807511737089, 'f1': 0.8146453089244852, 'number': 1065} | 0.7488            | 0.7988         | 0.7730     | 0.8064           |
| 0.0794        | 12.0  | 120  | 0.9323          | {'precision': 0.7244785949506037, 'recall': 0.8158220024721878, 'f1': 0.7674418604651163, 'number': 809} | {'precision': 0.4172661870503597, 'recall': 0.48739495798319327, 'f1': 0.4496124031007752, 'number': 119}   | {'precision': 0.8152985074626866, 'recall': 0.8206572769953052, 'f1': 0.8179691155825924, 'number': 1065} | 0.7502            | 0.7988         | 0.7738     | 0.8094           |
| 0.0803        | 13.0  | 130  | 0.9429          | {'precision': 0.7401129943502824, 'recall': 0.8096415327564895, 'f1': 0.7733175914994096, 'number': 809} | {'precision': 0.42592592592592593, 'recall': 0.5798319327731093, 'f1': 0.49110320284697506, 'number': 119}  | {'precision': 0.8110599078341014, 'recall': 0.8262910798122066, 'f1': 0.8186046511627907, 'number': 1065} | 0.7523            | 0.8048         | 0.7777     | 0.8085           |
| 0.0754        | 14.0  | 140  | 0.9393          | {'precision': 0.7425629290617849, 'recall': 0.8022249690976514, 'f1': 0.7712418300653594, 'number': 809} | {'precision': 0.4225352112676056, 'recall': 0.5042016806722689, 'f1': 0.45977011494252873, 'number': 119}   | {'precision': 0.8018099547511313, 'recall': 0.831924882629108, 'f1': 0.816589861751152, 'number': 1065}   | 0.7520            | 0.8003         | 0.7754     | 0.8106           |
| 0.0732        | 15.0  | 150  | 0.9422          | {'precision': 0.7382857142857143, 'recall': 0.7985166872682324, 'f1': 0.7672209026128266, 'number': 809} | {'precision': 0.42758620689655175, 'recall': 0.5210084033613446, 'f1': 0.4696969696969697, 'number': 119}   | {'precision': 0.8075160403299725, 'recall': 0.8272300469483568, 'f1': 0.8172541743970314, 'number': 1065} | 0.7527            | 0.7973         | 0.7744     | 0.8096           |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "datasets": ["funsd"], "model-index": [{"name": "layoutmlv2_funsd_rjz", "results": []}]}
transformers pytorch tensorboard safetensors layoutlm token-classification generated_from_trainer dataset:funsd autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #layoutlm #token-classification #generated_from_trainer #dataset-funsd #autotrain_compatible #endpoints_compatible #region-us 

layoutmlv2\_funsd\_rjz
======================


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


* Loss: 0.9422
* Answer: {'precision': 0.7382857142857143, 'recall': 0.7985166872682324, 'f1': 0.7672209026128266, 'number': 809}
* Header: {'precision': 0.42758620689655175, 'recall': 0.5210084033613446, 'f1': 0.4696969696969697, 'number': 119}
* Question: {'precision': 0.8075160403299725, 'recall': 0.8272300469483568, 'f1': 0.8172541743970314, 'number': 1065}
* Overall Precision: 0.7527
* Overall Recall: 0.7973
* Overall F1: 0.7744
* Overall Accuracy: 0.8096


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: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #layoutlm #token-classification #generated_from_trainer #dataset-funsd #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #layoutlm #token-classification #generated_from_trainer #dataset-funsd #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2