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LiLT-RE-FR
kavg/LiLT-RE-FR
LiLT-RE-FR is a machine learning model from kavg. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- mit
- Model type
- lilt
- Access
- Public
- Created
- Mar 30, 2024
- Updated
- Mar 30, 2024
- SHA
- 0bf9a238
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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. -->
checkpoints
This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base on the xfun dataset. It achieves the following results on the evaluation set:
- Precision: 0.3126
- Recall: 0.6777
- F1: 0.4278
- Loss: 0.5651
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: 10000
Training results
| Training Loss | Epoch | Step | F1 | Validation Loss | Precision | Recall |
|---|---|---|---|---|---|---|
| 0.2058 | 19.23 | 500 | 0 | 0.2763 | 0 | 0 |
| 0.145 | 38.46 | 1000 | 0.0623 | 0.2325 | 0.2889 | 0.0349 |
| 0.1441 | 57.69 | 1500 | 0.1232 | 0.2306 | 0.2616 | 0.0806 |
| 0.0902 | 76.92 | 2000 | 0.2645 | 0.2439 | 0.2526 | 0.2775 |
| 0.0768 | 96.15 | 2500 | 0.3176 | 0.3033 | 0.2440 | 0.4548 |
| 0.0707 | 115.38 | 3000 | 0.3472 | 0.3333 | 0.2778 | 0.4628 |
| 0.0649 | 134.62 | 3500 | 0.3509 | 0.3677 | 0.2629 | 0.5273 |
| 0.0257 | 153.85 | 4000 | 0.3705 | 0.4219 | 0.2810 | 0.5434 |
| 0.054 | 173.08 | 4500 | 0.3699 | 0.4440 | 0.2729 | 0.5739 |
| 0.0368 | 192.31 | 5000 | 0.3942 | 0.4843 | 0.3005 | 0.5730 |
| 0.0326 | 211.54 | 5500 | 0.3968 | 0.4651 | 0.2952 | 0.6052 |
| 0.0412 | 230.77 | 6000 | 0.4100 | 0.5386 | 0.3018 | 0.6392 |
| 0.0603 | 250.0 | 6500 | 0.4189 | 0.4957 | 0.3068 | 0.6598 |
| 0.0215 | 269.23 | 7000 | 0.4127 | 0.4768 | 0.2999 | 0.6616 |
| 0.0233 | 288.46 | 7500 | 0.4284 | 0.5245 | 0.3183 | 0.6553 |
| 0.0212 | 307.69 | 8000 | 0.4259 | 0.5424 | 0.3091 | 0.6849 |
| 0.0152 | 326.92 | 8500 | 0.4206 | 0.5655 | 0.3073 | 0.6661 |
| 0.0147 | 346.15 | 9000 | 0.4260 | 0.5630 | 0.3123 | 0.6697 |
| 0.0205 | 365.38 | 9500 | 0.4321 | 0.5389 | 0.3174 | 0.6768 |
| 0.0115 | 384.62 | 10000 | 0.4278 | 0.5651 | 0.3126 | 0.6777 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
- Library
- transformers
- Type
- lilt
- License
- mit
- Created
- Mar 30, 2024
- Updated
- Mar 30, 2024