---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LayoutLMv3_5_entities
  results: []
---

<!-- 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. -->

# LayoutLMv3_5_entities

This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1966
- Precision: 0.8679
- Recall: 0.8519
- F1: 0.8598
- Accuracy: 0.9772

## 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.56  | 100  | 0.1209          | 0.8602    | 0.7407 | 0.7960 | 0.9666   |
| No log        | 5.13  | 200  | 0.1267          | 0.8365    | 0.8056 | 0.8208 | 0.9710   |
| No log        | 7.69  | 300  | 0.1673          | 0.8830    | 0.7685 | 0.8218 | 0.9701   |
| No log        | 10.26 | 400  | 0.1428          | 0.8911    | 0.8333 | 0.8612 | 0.9745   |
| 0.0687        | 12.82 | 500  | 0.1457          | 0.8636    | 0.8796 | 0.8716 | 0.9763   |
| 0.0687        | 15.38 | 600  | 0.1854          | 0.9062    | 0.8056 | 0.8529 | 0.9754   |
| 0.0687        | 17.95 | 700  | 0.1841          | 0.8835    | 0.8426 | 0.8626 | 0.9772   |
| 0.0687        | 20.51 | 800  | 0.1728          | 0.8505    | 0.8426 | 0.8465 | 0.9754   |
| 0.0687        | 23.08 | 900  | 0.1986          | 0.8505    | 0.8426 | 0.8465 | 0.9745   |
| 0.0038        | 25.64 | 1000 | 0.2087          | 0.8558    | 0.8241 | 0.8396 | 0.9737   |
| 0.0038        | 28.21 | 1100 | 0.1949          | 0.8545    | 0.8704 | 0.8624 | 0.9772   |
| 0.0038        | 30.77 | 1200 | 0.1954          | 0.8532    | 0.8611 | 0.8571 | 0.9763   |
| 0.0038        | 33.33 | 1300 | 0.1912          | 0.8624    | 0.8704 | 0.8664 | 0.9781   |
| 0.0038        | 35.9  | 1400 | 0.1926          | 0.8611    | 0.8611 | 0.8611 | 0.9772   |
| 0.0003        | 38.46 | 1500 | 0.1969          | 0.8692    | 0.8611 | 0.8651 | 0.9763   |
| 0.0003        | 41.03 | 1600 | 0.1979          | 0.8611    | 0.8611 | 0.8611 | 0.9772   |
| 0.0003        | 43.59 | 1700 | 0.1976          | 0.8598    | 0.8519 | 0.8558 | 0.9763   |
| 0.0003        | 46.15 | 1800 | 0.1979          | 0.8598    | 0.8519 | 0.8558 | 0.9763   |
| 0.0003        | 48.72 | 1900 | 0.1979          | 0.8679    | 0.8519 | 0.8598 | 0.9772   |
| 0.0001        | 51.28 | 2000 | 0.1966          | 0.8679    | 0.8519 | 0.8598 | 0.9772   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3