xlm-roberta-base-finetuned-ner-thesis-dseb
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6324
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7314
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.5604 | 1.0 | 12 | 0.2449 | 0.0 | 0.0 | 0.0 | 0.9593 |
0.141 | 2.0 | 24 | 0.2360 | 0.0 | 0.0 | 0.0 | 0.9593 |
0.1111 | 3.0 | 36 | 0.2263 | 0.0 | 0.0 | 0.0 | 0.9593 |
0.0939 | 4.0 | 48 | 0.1960 | 0.0 | 0.0 | 0.0 | 0.9593 |
0.1069 | 5.0 | 60 | 0.1867 | 0.0 | 0.0 | 0.0 | 0.9593 |
0.0818 | 6.0 | 72 | 0.1879 | 0.0 | 0.0 | 0.0 | 0.9593 |
0.0614 | 7.0 | 84 | 0.1510 | 0.0 | 0.0 | 0.0 | 0.9593 |
0.0622 | 8.0 | 96 | 0.1348 | 0.0 | 0.0 | 0.0 | 0.9593 |
0.0633 | 9.0 | 108 | 0.1674 | 0.0 | 0.0 | 0.0 | 0.9593 |
0.0427 | 10.0 | 120 | 0.1632 | 0.0 | 0.0 | 0.0 | 0.9593 |
0.0389 | 11.0 | 132 | 0.1546 | 0.0 | 0.0 | 0.0 | 0.9590 |
0.0344 | 12.0 | 144 | 0.1754 | 0.125 | 0.0312 | 0.05 | 0.9618 |
0.0342 | 13.0 | 156 | 0.1702 | 0.0 | 0.0 | 0.0 | 0.9611 |
0.0272 | 14.0 | 168 | 0.1739 | 0.0526 | 0.0312 | 0.0392 | 0.9623 |
0.0224 | 15.0 | 180 | 0.1759 | 0.0526 | 0.0312 | 0.0392 | 0.9630 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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