output_LiLT_test_04
This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1349
- Precision: 0.7800
- Recall: 0.7950
- F1: 0.7874
- Accuracy: 0.9620
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: 300
- num_epochs: 60
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 0.0808 | 100 | 0.5003 | 0.1450 | 0.0360 | 0.0577 | 0.8851 |
No log | 0.1617 | 200 | 0.3188 | 0.4026 | 0.4142 | 0.4084 | 0.9031 |
No log | 0.2425 | 300 | 0.2046 | 0.6217 | 0.5907 | 0.6058 | 0.9355 |
No log | 0.3234 | 400 | 0.2054 | 0.6696 | 0.6647 | 0.6671 | 0.9404 |
0.3884 | 0.4042 | 500 | 0.1591 | 0.6765 | 0.7486 | 0.7107 | 0.9505 |
0.3884 | 0.4850 | 600 | 0.1795 | 0.6579 | 0.7391 | 0.6962 | 0.9394 |
0.3884 | 0.5659 | 700 | 0.1602 | 0.6660 | 0.8193 | 0.7347 | 0.9461 |
0.3884 | 0.6467 | 800 | 0.1879 | 0.6634 | 0.8129 | 0.7306 | 0.9439 |
0.3884 | 0.7276 | 900 | 0.1450 | 0.7756 | 0.8040 | 0.7895 | 0.9606 |
0.0964 | 0.8084 | 1000 | 0.1399 | 0.7652 | 0.7901 | 0.7775 | 0.9591 |
0.0964 | 0.8892 | 1100 | 0.1483 | 0.7767 | 0.7743 | 0.7755 | 0.9574 |
0.0964 | 0.9701 | 1200 | 0.1513 | 0.8295 | 0.7642 | 0.7955 | 0.9618 |
0.0964 | 1.0509 | 1300 | 0.1395 | 0.8197 | 0.7861 | 0.8025 | 0.9644 |
0.0964 | 1.1318 | 1400 | 0.1397 | 0.7436 | 0.8149 | 0.7776 | 0.9599 |
0.0762 | 1.2126 | 1500 | 0.1244 | 0.7928 | 0.8040 | 0.7983 | 0.9627 |
0.0762 | 1.2935 | 1600 | 0.1388 | 0.7541 | 0.8397 | 0.7946 | 0.9610 |
0.0762 | 1.3743 | 1700 | 0.1242 | 0.8207 | 0.8037 | 0.8121 | 0.9660 |
0.0762 | 1.4551 | 1800 | 0.1429 | 0.7529 | 0.8334 | 0.7911 | 0.9589 |
0.0762 | 1.5360 | 1900 | 0.1349 | 0.7800 | 0.7950 | 0.7874 | 0.9620 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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