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