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scenario-no_kd_weight_copy-data-smsa-model-xlm-roberta-base_trained
This model is a fine-tuned version of xlm-roberta-base on the smsa dataset. It achieves the following results on the evaluation set:
- Loss: 0.4707
- Accuracy: 0.9190
- F1: 0.8781
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6969
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
No log | 0.29 | 100 | 0.4823 | 0.8008 | 0.7617 |
No log | 0.58 | 200 | 0.3225 | 0.8778 | 0.8369 |
No log | 0.87 | 300 | 0.3690 | 0.8849 | 0.8278 |
No log | 1.16 | 400 | 0.3755 | 0.8960 | 0.8356 |
0.3796 | 1.45 | 500 | 0.2489 | 0.9079 | 0.8678 |
0.3796 | 1.74 | 600 | 0.3428 | 0.8929 | 0.8187 |
0.3796 | 2.03 | 700 | 0.2770 | 0.9143 | 0.8762 |
0.3796 | 2.33 | 800 | 0.2837 | 0.9222 | 0.8833 |
0.3796 | 2.62 | 900 | 0.2993 | 0.9040 | 0.8647 |
0.1998 | 2.91 | 1000 | 0.2754 | 0.9143 | 0.8786 |
0.1998 | 3.2 | 1100 | 0.2911 | 0.9135 | 0.8684 |
0.1998 | 3.49 | 1200 | 0.2835 | 0.9143 | 0.8750 |
0.1998 | 3.78 | 1300 | 0.3154 | 0.9143 | 0.8685 |
0.1998 | 4.07 | 1400 | 0.3486 | 0.9143 | 0.8758 |
0.1232 | 4.36 | 1500 | 0.3686 | 0.8960 | 0.8590 |
0.1232 | 4.65 | 1600 | 0.3097 | 0.9214 | 0.8879 |
0.1232 | 4.94 | 1700 | 0.3459 | 0.9262 | 0.8915 |
0.1232 | 5.23 | 1800 | 0.3691 | 0.9206 | 0.8864 |
0.1232 | 5.52 | 1900 | 0.4035 | 0.9214 | 0.8895 |
0.101 | 5.81 | 2000 | 0.3808 | 0.9167 | 0.8664 |
0.101 | 6.1 | 2100 | 0.4416 | 0.9143 | 0.8664 |
0.101 | 6.4 | 2200 | 0.4233 | 0.9159 | 0.8734 |
0.101 | 6.69 | 2300 | 0.4519 | 0.9095 | 0.8629 |
0.101 | 6.98 | 2400 | 0.3996 | 0.9175 | 0.8733 |
0.0628 | 7.27 | 2500 | 0.5061 | 0.9135 | 0.8619 |
0.0628 | 7.56 | 2600 | 0.4697 | 0.9167 | 0.8762 |
0.0628 | 7.85 | 2700 | 0.4646 | 0.9119 | 0.8688 |
0.0628 | 8.14 | 2800 | 0.5013 | 0.9167 | 0.8741 |
0.0628 | 8.43 | 2900 | 0.5132 | 0.9151 | 0.8712 |
0.0468 | 8.72 | 3000 | 0.4650 | 0.9198 | 0.8783 |
0.0468 | 9.01 | 3100 | 0.5350 | 0.9048 | 0.8405 |
0.0468 | 9.3 | 3200 | 0.4707 | 0.9190 | 0.8781 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3