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# layoutlm-funsd

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: 0.6657
- Answer: {'precision': 0.7226519337016575, 'recall': 0.8084054388133498, 'f1': 0.763127187864644, 'number': 809}
- Header: {'precision': 0.29545454545454547, 'recall': 0.3277310924369748, 'f1': 0.3107569721115538, 'number': 119}
- Question: {'precision': 0.7931960608773501, 'recall': 0.831924882629108, 'f1': 0.8120989917506873, 'number': 1065}
- Overall Precision: 0.7331
- Overall Recall: 0.7923
- Overall F1: 0.7615
- Overall Accuracy: 0.8136

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7892        | 1.0   | 10   | 1.6086          | {'precision': 0.020948180815876516, 'recall': 0.023485784919653894, 'f1': 0.022144522144522148, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.20356472795497185, 'recall': 0.20375586854460093, 'f1': 0.20366025340215863, 'number': 1065} | 0.1196            | 0.1184         | 0.1190     | 0.3742           |
| 1.4438        | 2.0   | 20   | 1.2175          | {'precision': 0.22015915119363394, 'recall': 0.20519159456118666, 'f1': 0.2124120281509917, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.4544037412314887, 'recall': 0.5474178403755868, 'f1': 0.4965928449744464, 'number': 1065}    | 0.3677            | 0.3758         | 0.3717     | 0.5883           |
| 1.0512        | 3.0   | 30   | 0.9159          | {'precision': 0.5192519251925193, 'recall': 0.5834363411619283, 'f1': 0.5494761350407451, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.6103327495621717, 'recall': 0.6544600938967137, 'f1': 0.6316266425011329, 'number': 1065}    | 0.5615            | 0.5866         | 0.5737     | 0.7102           |
| 0.8045        | 4.0   | 40   | 0.7549          | {'precision': 0.6132264529058116, 'recall': 0.7564894932014833, 'f1': 0.6773657996679578, 'number': 809}       | {'precision': 0.22, 'recall': 0.09243697478991597, 'f1': 0.13017751479289943, 'number': 119}               | {'precision': 0.6795580110497238, 'recall': 0.6929577464788732, 'f1': 0.6861924686192468, 'number': 1065}    | 0.6378            | 0.6829         | 0.6596     | 0.7538           |
| 0.6559        | 5.0   | 50   | 0.6887          | {'precision': 0.6546227417640808, 'recall': 0.761433868974042, 'f1': 0.704, 'number': 809}                     | {'precision': 0.25, 'recall': 0.16806722689075632, 'f1': 0.20100502512562815, 'number': 119}               | {'precision': 0.6964285714285714, 'recall': 0.7323943661971831, 'f1': 0.7139588100686498, 'number': 1065}    | 0.6614            | 0.7105         | 0.6851     | 0.7764           |
| 0.547         | 6.0   | 60   | 0.6515          | {'precision': 0.6659793814432989, 'recall': 0.7985166872682324, 'f1': 0.7262507026419337, 'number': 809}       | {'precision': 0.2891566265060241, 'recall': 0.20168067226890757, 'f1': 0.23762376237623764, 'number': 119} | {'precision': 0.7140439932318104, 'recall': 0.7924882629107981, 'f1': 0.7512238540275923, 'number': 1065}    | 0.6774            | 0.7597         | 0.7162     | 0.7928           |
| 0.4923        | 7.0   | 70   | 0.6337          | {'precision': 0.6784188034188035, 'recall': 0.7849196538936959, 'f1': 0.7277936962750717, 'number': 809}       | {'precision': 0.2761904761904762, 'recall': 0.24369747899159663, 'f1': 0.2589285714285714, 'number': 119}  | {'precision': 0.7371575342465754, 'recall': 0.8084507042253521, 'f1': 0.7711598746081505, 'number': 1065}    | 0.6904            | 0.7652         | 0.7258     | 0.8052           |
| 0.4463        | 8.0   | 80   | 0.6478          | {'precision': 0.7045454545454546, 'recall': 0.7663782447466008, 'f1': 0.7341622261693309, 'number': 809}       | {'precision': 0.2831858407079646, 'recall': 0.2689075630252101, 'f1': 0.27586206896551724, 'number': 119}  | {'precision': 0.751937984496124, 'recall': 0.819718309859155, 'f1': 0.7843665768194069, 'number': 1065}      | 0.7080            | 0.7652         | 0.7355     | 0.8048           |
| 0.3974        | 9.0   | 90   | 0.6389          | {'precision': 0.7029379760609358, 'recall': 0.7985166872682324, 'f1': 0.7476851851851851, 'number': 809}       | {'precision': 0.2748091603053435, 'recall': 0.3025210084033613, 'f1': 0.288, 'number': 119}                | {'precision': 0.7609254498714653, 'recall': 0.8338028169014085, 'f1': 0.7956989247311828, 'number': 1065}    | 0.7082            | 0.7878         | 0.7458     | 0.8060           |
| 0.3599        | 10.0  | 100  | 0.6429          | {'precision': 0.7177777777777777, 'recall': 0.7985166872682324, 'f1': 0.7559976594499708, 'number': 809}       | {'precision': 0.2966101694915254, 'recall': 0.29411764705882354, 'f1': 0.2953586497890296, 'number': 119}  | {'precision': 0.7795275590551181, 'recall': 0.8366197183098592, 'f1': 0.8070652173913043, 'number': 1065}    | 0.7274            | 0.7888         | 0.7569     | 0.8139           |
| 0.3227        | 11.0  | 110  | 0.6510          | {'precision': 0.710239651416122, 'recall': 0.8059332509270705, 'f1': 0.755066589461494, 'number': 809}         | {'precision': 0.28205128205128205, 'recall': 0.2773109243697479, 'f1': 0.2796610169491525, 'number': 119}  | {'precision': 0.7882037533512064, 'recall': 0.828169014084507, 'f1': 0.8076923076923077, 'number': 1065}     | 0.7275            | 0.7863         | 0.7557     | 0.8111           |
| 0.3156        | 12.0  | 120  | 0.6579          | {'precision': 0.7245575221238938, 'recall': 0.8096415327564895, 'f1': 0.7647402218330415, 'number': 809}       | {'precision': 0.2920353982300885, 'recall': 0.2773109243697479, 'f1': 0.28448275862068967, 'number': 119}  | {'precision': 0.7926391382405745, 'recall': 0.8291079812206573, 'f1': 0.8104635153740247, 'number': 1065}    | 0.7372            | 0.7883         | 0.7619     | 0.8123           |
| 0.2935        | 13.0  | 130  | 0.6596          | {'precision': 0.7119386637458927, 'recall': 0.8034610630407911, 'f1': 0.7549361207897795, 'number': 809}       | {'precision': 0.2846715328467153, 'recall': 0.3277310924369748, 'f1': 0.3046875, 'number': 119}            | {'precision': 0.7852112676056338, 'recall': 0.8375586854460094, 'f1': 0.8105406633348478, 'number': 1065}    | 0.7232            | 0.7933         | 0.7566     | 0.8131           |
| 0.2814        | 14.0  | 140  | 0.6629          | {'precision': 0.7189901207464325, 'recall': 0.8096415327564895, 'f1': 0.7616279069767442, 'number': 809}       | {'precision': 0.2923076923076923, 'recall': 0.31932773109243695, 'f1': 0.3052208835341365, 'number': 119}  | {'precision': 0.7924528301886793, 'recall': 0.828169014084507, 'f1': 0.8099173553719008, 'number': 1065}     | 0.7312            | 0.7903         | 0.7596     | 0.8132           |
| 0.2762        | 15.0  | 150  | 0.6657          | {'precision': 0.7226519337016575, 'recall': 0.8084054388133498, 'f1': 0.763127187864644, 'number': 809}        | {'precision': 0.29545454545454547, 'recall': 0.3277310924369748, 'f1': 0.3107569721115538, 'number': 119}  | {'precision': 0.7931960608773501, 'recall': 0.831924882629108, 'f1': 0.8120989917506873, 'number': 1065}     | 0.7331            | 0.7923         | 0.7615     | 0.8136           |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

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

layoutlm-funsd
==============


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: 0.6657
* Answer: {'precision': 0.7226519337016575, 'recall': 0.8084054388133498, 'f1': 0.763127187864644, 'number': 809}
* Header: {'precision': 0.29545454545454547, 'recall': 0.3277310924369748, 'f1': 0.3107569721115538, 'number': 119}
* Question: {'precision': 0.7931960608773501, 'recall': 0.831924882629108, 'f1': 0.8120989917506873, 'number': 1065}
* Overall Precision: 0.7331
* Overall Recall: 0.7923
* Overall F1: 0.7615
* Overall Accuracy: 0.8136


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 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #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 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3