metadata
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- funsd_re
metrics:
- precision
- recall
- f1
model-index:
- name: checkpoints
results: []
checkpoints
This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base on the funsd_re dataset. It achieves the following results on the evaluation set:
- Precision: 0.3264
- Recall: 0.4864
- F1: 0.3907
- Loss: 0.4377
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: 8
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | F1 | Validation Loss | Precision | Recall |
|---|---|---|---|---|---|---|
| 0.1604 | 26.32 | 500 | 0 | 0.1513 | 0 | 0 |
| 0.1012 | 52.63 | 1000 | 0.0098 | 0.0786 | 0.5 | 0.0049 |
| 0.0994 | 78.95 | 1500 | 0.2518 | 0.1847 | 0.3729 | 0.1901 |
| 0.0694 | 105.26 | 2000 | 0.3499 | 0.1926 | 0.3667 | 0.3346 |
| 0.0771 | 131.58 | 2500 | 0.3856 | 0.3295 | 0.3450 | 0.4370 |
| 0.0565 | 157.89 | 3000 | 0.3865 | 0.4137 | 0.3293 | 0.4679 |
| 0.0411 | 184.21 | 3500 | 0.3808 | 0.3624 | 0.3252 | 0.4593 |
| 0.0463 | 210.53 | 4000 | 0.3832 | 0.5089 | 0.3221 | 0.4728 |
| 0.0414 | 236.84 | 4500 | 0.3911 | 0.6137 | 0.3305 | 0.4790 |
| 0.036 | 263.16 | 5000 | 0.3910 | 0.4428 | 0.3275 | 0.4852 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1