
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# perioli_manifesti_v5.6

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: 0.0271
- Precision: 0.9305
- Recall: 0.9623
- F1: 0.9461
- Accuracy: 0.9934

## 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: 3500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.47  | 100  | 0.1846          | 0.6553    | 0.7448 | 0.6972 | 0.9597   |
| No log        | 0.93  | 200  | 0.0547          | 0.8780    | 0.9182 | 0.8977 | 0.9865   |
| No log        | 1.4   | 300  | 0.0442          | 0.9007    | 0.9371 | 0.9185 | 0.9895   |
| No log        | 1.87  | 400  | 0.0446          | 0.8823    | 0.9362 | 0.9085 | 0.9886   |
| 0.1459        | 2.34  | 500  | 0.0360          | 0.8995    | 0.9488 | 0.9235 | 0.9908   |
| 0.1459        | 2.8   | 600  | 0.0315          | 0.9205    | 0.9569 | 0.9383 | 0.9923   |
| 0.1459        | 3.27  | 700  | 0.0309          | 0.9316    | 0.9668 | 0.9489 | 0.9937   |
| 0.1459        | 3.74  | 800  | 0.0259          | 0.9121    | 0.9506 | 0.9309 | 0.9918   |
| 0.1459        | 4.21  | 900  | 0.0267          | 0.9264    | 0.9614 | 0.9436 | 0.9941   |
| 0.0247        | 4.67  | 1000 | 0.0272          | 0.9282    | 0.9641 | 0.9458 | 0.9937   |
| 0.0247        | 5.14  | 1100 | 0.0221          | 0.9355    | 0.9641 | 0.9496 | 0.9947   |
| 0.0247        | 5.61  | 1200 | 0.0236          | 0.9494    | 0.9614 | 0.9554 | 0.9946   |
| 0.0247        | 6.07  | 1300 | 0.0331          | 0.9050    | 0.9416 | 0.9229 | 0.9898   |
| 0.0247        | 6.54  | 1400 | 0.0199          | 0.9278    | 0.9587 | 0.9430 | 0.9936   |
| 0.016         | 7.01  | 1500 | 0.0180          | 0.9465    | 0.9533 | 0.9499 | 0.9947   |
| 0.016         | 7.48  | 1600 | 0.0236          | 0.9437    | 0.9641 | 0.9538 | 0.9951   |
| 0.016         | 7.94  | 1700 | 0.0220          | 0.9359    | 0.9704 | 0.9528 | 0.9946   |
| 0.016         | 8.41  | 1800 | 0.0271          | 0.9185    | 0.9623 | 0.9399 | 0.9923   |
| 0.016         | 8.88  | 1900 | 0.0315          | 0.9294    | 0.9695 | 0.9490 | 0.9936   |
| 0.011         | 9.35  | 2000 | 0.0305          | 0.9186    | 0.9632 | 0.9404 | 0.9926   |
| 0.011         | 9.81  | 2100 | 0.0286          | 0.9271    | 0.9596 | 0.9430 | 0.9930   |
| 0.011         | 10.28 | 2200 | 0.0242          | 0.9296    | 0.9497 | 0.9396 | 0.9928   |
| 0.011         | 10.75 | 2300 | 0.0316          | 0.9183    | 0.9488 | 0.9333 | 0.9908   |
| 0.011         | 11.21 | 2400 | 0.0293          | 0.9311    | 0.9596 | 0.9451 | 0.9932   |
| 0.0069        | 11.68 | 2500 | 0.0285          | 0.9297    | 0.9632 | 0.9462 | 0.9932   |
| 0.0069        | 12.15 | 2600 | 0.0309          | 0.9114    | 0.9524 | 0.9315 | 0.9918   |
| 0.0069        | 12.62 | 2700 | 0.0290          | 0.9206    | 0.9587 | 0.9393 | 0.9928   |
| 0.0069        | 13.08 | 2800 | 0.0270          | 0.9323    | 0.9650 | 0.9483 | 0.9937   |
| 0.0069        | 13.55 | 2900 | 0.0249          | 0.9363    | 0.9641 | 0.9500 | 0.9941   |
| 0.0059        | 14.02 | 3000 | 0.0273          | 0.9297    | 0.9632 | 0.9462 | 0.9933   |
| 0.0059        | 14.49 | 3100 | 0.0271          | 0.9278    | 0.9587 | 0.9430 | 0.9930   |
| 0.0059        | 14.95 | 3200 | 0.0297          | 0.9248    | 0.9614 | 0.9427 | 0.9928   |
| 0.0059        | 15.42 | 3300 | 0.0275          | 0.9305    | 0.9623 | 0.9461 | 0.9933   |
| 0.0059        | 15.89 | 3400 | 0.0275          | 0.9296    | 0.9605 | 0.9448 | 0.9933   |
| 0.0047        | 16.36 | 3500 | 0.0271          | 0.9305    | 0.9623 | 0.9461 | 0.9934   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "perioli_manifesti_v5.6", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9304952215464813, "name": "Precision"}, {"type": "recall", "value": 0.9622641509433962, "name": "Recall"}, {"type": "f1", "value": 0.9461130742049471, "name": "F1"}, {"type": "accuracy", "value": 0.9934494773519164, "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\_v5.6
========================


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: 0.0271
* Precision: 0.9305
* Recall: 0.9623
* F1: 0.9461
* Accuracy: 0.9934


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: 3500


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3



### 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: 3500 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3
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: 3500 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3