---
tags:
- generated_from_trainer
datasets:
- funsd-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: lilt-roberta-en-base-finetuned-funsd
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: funsd-layoutlmv3
      type: funsd-layoutlmv3
      config: funsd
      split: train
      args: funsd
    metrics:
    - name: Precision
      type: precision
      value: 0.8761670761670761
    - name: Recall
      type: recall
      value: 0.8857426726279185
    - name: F1
      type: f1
      value: 0.8809288537549407
    - name: Accuracy
      type: accuracy
      value: 0.8068465470105789
inference: false
---

<!-- 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. -->

# lilt-roberta-en-base-finetuned-funsd

This model is a fine-tuned version of [nielsr/lilt-roberta-en-base](https://huggingface.co/nielsr/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6552
- Precision: 0.8762
- Recall: 0.8857
- F1: 0.8809
- Accuracy: 0.8068

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- training_steps: 2000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 5.26   | 100  | 1.1789          | 0.8506    | 0.8485 | 0.8495 | 0.7869   |
| No log        | 10.53  | 200  | 1.2382          | 0.8360    | 0.8788 | 0.8569 | 0.7970   |
| No log        | 15.79  | 300  | 1.3766          | 0.8557    | 0.8897 | 0.8724 | 0.7909   |
| No log        | 21.05  | 400  | 1.5590          | 0.8368    | 0.8763 | 0.8561 | 0.7792   |
| 0.04          | 26.32  | 500  | 1.4379          | 0.8562    | 0.8813 | 0.8685 | 0.7992   |
| 0.04          | 31.58  | 600  | 1.5397          | 0.8593    | 0.8947 | 0.8766 | 0.8054   |
| 0.04          | 36.84  | 700  | 1.6132          | 0.8621    | 0.8723 | 0.8672 | 0.7933   |
| 0.04          | 42.11  | 800  | 1.6483          | 0.8566    | 0.8872 | 0.8716 | 0.7777   |
| 0.04          | 47.37  | 900  | 1.6593          | 0.8641    | 0.8813 | 0.8726 | 0.7895   |
| 0.0044        | 52.63  | 1000 | 1.6704          | 0.8595    | 0.8718 | 0.8656 | 0.7925   |
| 0.0044        | 57.89  | 1100 | 1.6795          | 0.8495    | 0.8803 | 0.8646 | 0.7748   |
| 0.0044        | 63.16  | 1200 | 1.5515          | 0.8604    | 0.8912 | 0.8755 | 0.7991   |
| 0.0044        | 68.42  | 1300 | 1.6665          | 0.8573    | 0.8867 | 0.8718 | 0.7821   |
| 0.0044        | 73.68  | 1400 | 1.5893          | 0.8604    | 0.8877 | 0.8738 | 0.7895   |
| 0.0008        | 78.95  | 1500 | 1.5613          | 0.8603    | 0.8872 | 0.8736 | 0.8123   |
| 0.0008        | 84.21  | 1600 | 1.5853          | 0.8521    | 0.8872 | 0.8693 | 0.8040   |
| 0.0008        | 89.47  | 1700 | 1.6539          | 0.8707    | 0.8833 | 0.8769 | 0.8077   |
| 0.0008        | 94.74  | 1800 | 1.6634          | 0.8787    | 0.8813 | 0.8800 | 0.8079   |
| 0.0008        | 100.0  | 1900 | 1.6534          | 0.8810    | 0.8862 | 0.8836 | 0.8073   |
| 0.0004        | 105.26 | 2000 | 1.6552          | 0.8762    | 0.8857 | 0.8809 | 0.8068   |


### Framework versions

- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.13.0