---
tags:
- generated_from_trainer
model-index:
- name: UDOP-finetuned-DocLayNet-3
  results: []
license: apache-2.0
datasets:
- pierreguillou/DocLayNet-small
language:
- en
---

<!-- 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. -->

# UDOP-finetuned-DocLayNet-3

This model is a fine-tuned version of [](https://huggingface.co./) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7407
- eval_precision: 0.6058
- eval_recall: 0.5870
- eval_f1: 0.5962
- eval_accuracy: 0.7863
- eval_runtime: 16.2128
- eval_samples_per_second: 3.886
- eval_steps_per_second: 1.974
- epoch: 18.6
- step: 800



## Training procedure
  ### Training code: 
      https://github.com/mit1280/Document-AI/blob/main/UDOPEncoderModel_fine_tune_DocLayNet.ipynb
  ### Inference code: 
      https://github.com/mit1280/Document-AI/blob/main/UDOP_DocLayNet_Inference.ipynb

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000

### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2