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xlm-roberta-large-finetuned-augument-visquad2-1-4-2023-1
This model is a fine-tuned version of xlm-roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Best F1: 77.2917
- Loss: 1.3981
- Exact: 39.8063
- F1: 57.2058
- Total: 3821
- Hasans Exact: 56.6152
- Hasans F1: 81.6749
- Hasans Total: 2653
- Noans Exact: 1.6267
- Noans F1: 1.6267
- Noans Total: 1168
- Best Exact: 61.4499
- Best Exact Thresh: 0.8562
- Best F1 Thresh: 0.8563
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Best F1 | Validation Loss | Exact | F1 | Total | Hasans Exact | Hasans F1 | Hasans Total | Noans Exact | Noans F1 | Noans Total | Best Exact | Best Exact Thresh | Best F1 Thresh |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.2156 | 1.0 | 2110 | 72.5368 | 1.1646 | 37.6341 | 55.4110 | 3821 | 54.1651 | 79.7684 | 2653 | 0.0856 | 0.0856 | 1168 | 57.3672 | 0.8787 | 0.9480 |
| 0.442 | 2.0 | 4221 | 75.8888 | 1.0343 | 38.7857 | 56.3275 | 3821 | 55.8613 | 81.1261 | 2653 | 0.0 | 0.0 | 1168 | 60.6386 | 0.8815 | 0.8837 |
| 0.3067 | 3.0 | 6332 | 76.3203 | 1.1121 | 39.7540 | 56.6409 | 3821 | 57.2559 | 81.5774 | 2653 | 0.0 | 0.0 | 1168 | 61.3975 | 0.7946 | 0.9089 |
| 0.2223 | 4.0 | 8443 | 76.6216 | 1.2653 | 39.6493 | 56.7375 | 3821 | 57.0675 | 81.6789 | 2653 | 0.0856 | 0.0856 | 1168 | 61.3190 | 0.7363 | 0.9385 |
| 0.1674 | 5.0 | 10550 | 77.2917 | 1.3981 | 39.8063 | 57.2058 | 3821 | 56.6152 | 81.6749 | 2653 | 1.6267 | 1.6267 | 1168 | 61.4499 | 0.8562 | 0.8563 |
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
