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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: 0.6978
- Answer: {'precision': 0.7016216216216217, 'recall': 0.8022249690976514, 'f1': 0.748558246828143, 'number': 809}
- Header: {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119}
- Question: {'precision': 0.7697022767075307, 'recall': 0.8253521126760563, 'f1': 0.7965564114182148, 'number': 1065}
- Overall Precision: 0.7149
- Overall Recall: 0.7852
- Overall F1: 0.7484
- Overall Accuracy: 0.7991

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8306        | 1.0   | 10   | 1.6241          | {'precision': 0.015978695073235686, 'recall': 0.014833127317676144, 'f1': 0.015384615384615385, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.18994413407821228, 'recall': 0.12769953051643193, 'f1': 0.1527231892195396, 'number': 1065} | 0.1009            | 0.0743         | 0.0855     | 0.3533           |
| 1.4993        | 2.0   | 20   | 1.2681          | {'precision': 0.11957950065703023, 'recall': 0.11248454882571075, 'f1': 0.11592356687898088, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.43696829079659705, 'recall': 0.5305164319248826, 'f1': 0.4792196776929601, 'number': 1065}  | 0.3192            | 0.3292         | 0.3241     | 0.5668           |
| 1.1217        | 3.0   | 30   | 0.9584          | {'precision': 0.4690157958687728, 'recall': 0.47713226205191595, 'f1': 0.47303921568627455, 'number': 809}     | {'precision': 0.08333333333333333, 'recall': 0.025210084033613446, 'f1': 0.038709677419354833, 'number': 119} | {'precision': 0.6071741032370953, 'recall': 0.6516431924882629, 'f1': 0.6286231884057971, 'number': 1065}   | 0.5410            | 0.5434         | 0.5422     | 0.7000           |
| 0.8403        | 4.0   | 40   | 0.7788          | {'precision': 0.6163265306122448, 'recall': 0.7466007416563659, 'f1': 0.6752375628842929, 'number': 809}       | {'precision': 0.2127659574468085, 'recall': 0.08403361344537816, 'f1': 0.12048192771084337, 'number': 119}    | {'precision': 0.6675603217158177, 'recall': 0.7014084507042253, 'f1': 0.6840659340659341, 'number': 1065}   | 0.6342            | 0.6829         | 0.6576     | 0.7495           |
| 0.6807        | 5.0   | 50   | 0.7110          | {'precision': 0.6525871172122492, 'recall': 0.7639060568603214, 'f1': 0.7038724373576309, 'number': 809}       | {'precision': 0.26865671641791045, 'recall': 0.15126050420168066, 'f1': 0.19354838709677416, 'number': 119}   | {'precision': 0.7077059344552702, 'recall': 0.7502347417840376, 'f1': 0.7283500455788514, 'number': 1065}   | 0.6696            | 0.7200         | 0.6939     | 0.7799           |
| 0.5615        | 6.0   | 60   | 0.6839          | {'precision': 0.6663135593220338, 'recall': 0.7775030902348579, 'f1': 0.7176269252709641, 'number': 809}       | {'precision': 0.3225806451612903, 'recall': 0.16806722689075632, 'f1': 0.22099447513812157, 'number': 119}    | {'precision': 0.7101214574898785, 'recall': 0.8234741784037559, 'f1': 0.7626086956521739, 'number': 1065}   | 0.6809            | 0.7657         | 0.7208     | 0.7886           |
| 0.4954        | 7.0   | 70   | 0.6647          | {'precision': 0.6813304721030042, 'recall': 0.7849196538936959, 'f1': 0.7294658242389431, 'number': 809}       | {'precision': 0.28865979381443296, 'recall': 0.23529411764705882, 'f1': 0.2592592592592593, 'number': 119}    | {'precision': 0.7263681592039801, 'recall': 0.8225352112676056, 'f1': 0.7714663143989432, 'number': 1065}   | 0.6886            | 0.7722         | 0.7280     | 0.7957           |
| 0.4479        | 8.0   | 80   | 0.6529          | {'precision': 0.6748663101604279, 'recall': 0.7799752781211372, 'f1': 0.7236238532110092, 'number': 809}       | {'precision': 0.25742574257425743, 'recall': 0.2184873949579832, 'f1': 0.23636363636363636, 'number': 119}    | {'precision': 0.740016992353441, 'recall': 0.8178403755868544, 'f1': 0.7769848349687779, 'number': 1065}    | 0.6905            | 0.7667         | 0.7266     | 0.8053           |
| 0.3961        | 9.0   | 90   | 0.6535          | {'precision': 0.6924754634678298, 'recall': 0.7849196538936959, 'f1': 0.7358053302433372, 'number': 809}       | {'precision': 0.25217391304347825, 'recall': 0.24369747899159663, 'f1': 0.24786324786324784, 'number': 119}   | {'precision': 0.7459505541346974, 'recall': 0.8215962441314554, 'f1': 0.7819481680071492, 'number': 1065}   | 0.6980            | 0.7722         | 0.7332     | 0.8020           |
| 0.3516        | 10.0  | 100  | 0.6645          | {'precision': 0.6899141630901288, 'recall': 0.7948084054388134, 'f1': 0.7386559448592762, 'number': 809}       | {'precision': 0.2857142857142857, 'recall': 0.25210084033613445, 'f1': 0.26785714285714285, 'number': 119}    | {'precision': 0.7667814113597247, 'recall': 0.8366197183098592, 'f1': 0.8001796138302649, 'number': 1065}   | 0.7112            | 0.7847         | 0.7462     | 0.8046           |
| 0.3197        | 11.0  | 110  | 0.6868          | {'precision': 0.6927194860813705, 'recall': 0.799752781211372, 'f1': 0.7423981640849111, 'number': 809}        | {'precision': 0.2966101694915254, 'recall': 0.29411764705882354, 'f1': 0.2953586497890296, 'number': 119}     | {'precision': 0.7722513089005235, 'recall': 0.8309859154929577, 'f1': 0.8005427408412483, 'number': 1065}   | 0.7129            | 0.7863         | 0.7478     | 0.7996           |
| 0.2986        | 12.0  | 120  | 0.6912          | {'precision': 0.6914778856526429, 'recall': 0.792336217552534, 'f1': 0.7384792626728109, 'number': 809}        | {'precision': 0.3162393162393162, 'recall': 0.31092436974789917, 'f1': 0.3135593220338983, 'number': 119}     | {'precision': 0.7845057880676759, 'recall': 0.8272300469483568, 'f1': 0.8053016453382084, 'number': 1065}   | 0.7194            | 0.7822         | 0.7495     | 0.7971           |
| 0.2882        | 13.0  | 130  | 0.7016          | {'precision': 0.6961748633879782, 'recall': 0.7873918417799752, 'f1': 0.7389791183294663, 'number': 809}       | {'precision': 0.3076923076923077, 'recall': 0.3025210084033613, 'f1': 0.30508474576271183, 'number': 119}     | {'precision': 0.7696969696969697, 'recall': 0.8347417840375587, 'f1': 0.8009009009009008, 'number': 1065}   | 0.7142            | 0.7837         | 0.7474     | 0.8028           |
| 0.2789        | 14.0  | 140  | 0.6994          | {'precision': 0.6989247311827957, 'recall': 0.8034610630407911, 'f1': 0.747556066705003, 'number': 809}        | {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119}    | {'precision': 0.7698343504795118, 'recall': 0.8291079812206573, 'f1': 0.7983725135623869, 'number': 1065}   | 0.7140            | 0.7878         | 0.7490     | 0.7990           |
| 0.274         | 15.0  | 150  | 0.6978          | {'precision': 0.7016216216216217, 'recall': 0.8022249690976514, 'f1': 0.748558246828143, 'number': 809}        | {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119}    | {'precision': 0.7697022767075307, 'recall': 0.8253521126760563, 'f1': 0.7965564114182148, 'number': 1065}   | 0.7149            | 0.7852         | 0.7484     | 0.7991           |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- 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: 0.6978
* Answer: {'precision': 0.7016216216216217, 'recall': 0.8022249690976514, 'f1': 0.748558246828143, 'number': 809}
* Header: {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119}
* Question: {'precision': 0.7697022767075307, 'recall': 0.8253521126760563, 'f1': 0.7965564114182148, 'number': 1065}
* Overall Precision: 0.7149
* Overall Recall: 0.7852
* Overall F1: 0.7484
* Overall Accuracy: 0.7991


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



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3