---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- nielsr/funsd-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pasha
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: nielsr/funsd-layoutlmv3
      type: nielsr/funsd-layoutlmv3
      config: pasha
      split: test
      args: pasha
    metrics:
    - name: Precision
      type: precision
      value: 0.8787771062922147
    - name: Recall
      type: recall
      value: 0.8904899135446686
    - name: F1
      type: f1
      value: 0.8845947396672033
    - name: Accuracy
      type: accuracy
      value: 0.9093539054966249
---

<!-- 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. -->

# pasha

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the nielsr/funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6479
- Precision: 0.8788
- Recall: 0.8905
- F1: 0.8846
- Accuracy: 0.9094

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.13  | 100  | 0.6479          | 0.8788    | 0.8905 | 0.8846 | 0.9094   |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.2