File size: 1,635 Bytes
f7b1cba 2e40517 f7b1cba 07290d7 f7b1cba |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
model-index:
- name: lilt-en-funsd
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. -->
# lilt-en-funsd
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9402
- Answer: {'precision': 0.4992163009404389, 'recall': 0.7796817625458996, 'f1': 0.608695652173913, 'number': 817}
- Header: {'precision': 0.03125, 'recall': 0.008403361344537815, 'f1': 0.013245033112582781, 'number': 119}
- Question: {'precision': 0.625, 'recall': 0.7753017641597029, 'f1': 0.692084542063821, 'number': 1077}
- Overall Precision: 0.5571
- Overall Recall: 0.7317
- Overall F1: 0.6326
- Overall Accuracy: 0.6359
## 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
- training_steps: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|