---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: docintel-extractor
  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. -->

# docintel-extractor

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4371
- Precision: 0.8960
- Recall: 0.9042
- F1: 0.9001
- Accuracy: 0.8642

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 19   | 2.3352          | 0.5368    | 0.4916 | 0.5132 | 0.5602   |
| No log        | 2.0   | 38   | 1.2024          | 0.7210    | 0.8358 | 0.7742 | 0.7811   |
| No log        | 3.0   | 57   | 1.0774          | 0.8171    | 0.8784 | 0.8466 | 0.8039   |
| No log        | 4.0   | 76   | 1.0967          | 0.8018    | 0.8530 | 0.8266 | 0.8176   |
| No log        | 5.0   | 95   | 1.1609          | 0.8495    | 0.8555 | 0.8525 | 0.8360   |
| No log        | 6.0   | 114  | 1.0465          | 0.8610    | 0.9133 | 0.8864 | 0.8575   |
| No log        | 7.0   | 133  | 1.1293          | 0.8822    | 0.9072 | 0.8946 | 0.8493   |
| No log        | 8.0   | 152  | 1.3029          | 0.8352    | 0.8525 | 0.8437 | 0.8229   |
| No log        | 9.0   | 171  | 1.2912          | 0.8836    | 0.8925 | 0.8880 | 0.8538   |
| No log        | 10.0  | 190  | 1.3468          | 0.8949    | 0.8976 | 0.8963 | 0.8371   |
| No log        | 11.0  | 209  | 1.4046          | 0.8896    | 0.8905 | 0.8901 | 0.8442   |
| No log        | 12.0  | 228  | 1.3960          | 0.9076    | 0.8915 | 0.8995 | 0.8619   |
| No log        | 13.0  | 247  | 1.4927          | 0.8991    | 0.8941 | 0.8966 | 0.8618   |
| No log        | 14.0  | 266  | 1.4369          | 0.8960    | 0.9042 | 0.9001 | 0.8642   |
| No log        | 15.0  | 285  | 1.4735          | 0.8989    | 0.8971 | 0.8980 | 0.8617   |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2