File size: 3,836 Bytes
d20f4f0 |
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 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
---
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-SIN
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xfun
type: xfun
config: xfun.sin
split: validation
args: xfun.sin
metrics:
- name: Precision
type: precision
value: 0.7058139534883721
- name: Recall
type: recall
value: 0.7475369458128078
- name: F1
type: f1
value: 0.7260765550239234
- name: Accuracy
type: accuracy
value: 0.8621124982465984
---
<!-- 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-SER-SIN
This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1967
- Precision: 0.7058
- Recall: 0.7475
- F1: 0.7261
- Accuracy: 0.8621
## 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: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.0739 | 21.74 | 500 | 0.8268 | 0.5620 | 0.7143 | 0.5 | 0.6416 |
| 0.0509 | 43.48 | 1000 | 0.8324 | 0.5839 | 0.8499 | 0.5348 | 0.6429 |
| 0.0004 | 65.22 | 1500 | 0.8398 | 0.6521 | 0.9889 | 0.6256 | 0.6810 |
| 0.0004 | 86.96 | 2000 | 0.8461 | 0.6678 | 1.0577 | 0.6251 | 0.7167 |
| 0.003 | 108.7 | 2500 | 0.8561 | 0.6929 | 1.0734 | 0.6532 | 0.7377 |
| 0.0006 | 130.43 | 3000 | 0.8569 | 0.6924 | 1.1114 | 0.6686 | 0.7180 |
| 0.0022 | 152.17 | 3500 | 0.8245 | 0.6749 | 1.4184 | 0.6774 | 0.6724 |
| 0.0001 | 173.91 | 4000 | 0.8502 | 0.6937 | 1.0524 | 0.6546 | 0.7377 |
| 0.001 | 195.65 | 4500 | 0.8493 | 0.6900 | 1.1949 | 0.6663 | 0.7155 |
| 0.0001 | 217.39 | 5000 | 0.8460 | 0.6885 | 1.1462 | 0.6790 | 0.6983 |
| 0.0001 | 239.13 | 5500 | 0.8641 | 0.6970 | 1.1296 | 0.6697 | 0.7266 |
| 0.0 | 260.87 | 6000 | 0.8529 | 0.7046 | 1.2585 | 0.6929 | 0.7167 |
| 0.0037 | 282.61 | 6500 | 0.8634 | 0.7139 | 1.2292 | 0.6917 | 0.7377 |
| 0.0 | 304.35 | 7000 | 0.8621 | 0.7261 | 1.1967 | 0.7058 | 0.7475 |
| 0.0 | 326.09 | 7500 | 0.8585 | 0.7230 | 1.2144 | 0.7089 | 0.7377 |
| 0.0 | 347.83 | 8000 | 0.8609 | 0.7180 | 1.2117 | 0.6918 | 0.7463 |
| 0.0 | 369.57 | 8500 | 0.8628 | 0.7135 | 1.1961 | 0.6755 | 0.7562 |
| 0.0 | 391.3 | 9000 | 0.8624 | 0.7220 | 1.2292 | 0.7059 | 0.7389 |
| 0.0 | 413.04 | 9500 | 0.8611 | 0.7262 | 1.2278 | 0.7071 | 0.7463 |
| 0.0 | 434.78 | 10000 | 0.8609 | 0.7242 | 1.2317 | 0.7056 | 0.7438 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|