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# perioli_manifesti_v3.6

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0893
- Precision: 0.9019
- Recall: 0.9223
- F1: 0.9120
- Accuracy: 0.9869

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.41  | 100  | 0.1184          | 0.8411    | 0.8920 | 0.8658 | 0.9817   |
| No log        | 2.82  | 200  | 0.0671          | 0.8821    | 0.9072 | 0.8945 | 0.9840   |
| No log        | 4.23  | 300  | 0.0739          | 0.8919    | 0.9223 | 0.9069 | 0.9863   |
| No log        | 5.63  | 400  | 0.0534          | 0.9270    | 0.9621 | 0.9442 | 0.9909   |
| 0.1242        | 7.04  | 500  | 0.0917          | 0.8947    | 0.9015 | 0.8981 | 0.9856   |
| 0.1242        | 8.45  | 600  | 0.0773          | 0.9065    | 0.9186 | 0.9125 | 0.9873   |
| 0.1242        | 9.86  | 700  | 0.0836          | 0.8950    | 0.9205 | 0.9076 | 0.9863   |
| 0.1242        | 11.27 | 800  | 0.0803          | 0.9112    | 0.9129 | 0.9120 | 0.9873   |
| 0.1242        | 12.68 | 900  | 0.0914          | 0.8953    | 0.9072 | 0.9012 | 0.9860   |
| 0.0099        | 14.08 | 1000 | 0.0687          | 0.9069    | 0.9223 | 0.9146 | 0.9873   |
| 0.0099        | 15.49 | 1100 | 0.0889          | 0.9035    | 0.9223 | 0.9128 | 0.9873   |
| 0.0099        | 16.9  | 1200 | 0.0924          | 0.8980    | 0.9167 | 0.9072 | 0.9866   |
| 0.0099        | 18.31 | 1300 | 0.0858          | 0.9052    | 0.9223 | 0.9137 | 0.9873   |
| 0.0099        | 19.72 | 1400 | 0.0893          | 0.9019    | 0.9223 | 0.9120 | 0.9869   |
| 0.0043        | 21.13 | 1500 | 0.0893          | 0.9019    | 0.9223 | 0.9120 | 0.9869   |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.2.2
- Tokenizers 0.13.2

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "perioli_manifesti_v3.6", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9018518518518519, "name": "Precision"}, {"type": "recall", "value": 0.9223484848484849, "name": "Recall"}, {"type": "f1", "value": 0.9119850187265918, "name": "F1"}, {"type": "accuracy", "value": 0.9869451697127938, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer dataset:sroie 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-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

perioli\_manifesti\_v3.6
========================


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


* Loss: 0.0893
* Precision: 0.9019
* Recall: 0.9223
* F1: 0.9120
* Accuracy: 0.9869


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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1500


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1500 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1500 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1500### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2