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scenario-non-kd-from-scratch-data-indolem_sentiment-model-xlm-roberta-base
This model is a fine-tuned version of xlm-roberta-base on the indolem_sentiment dataset. It achieves the following results on the evaluation set:
- Loss: 1.0766
- Accuracy: 0.8346
- F1: 0.7130
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 | 0.88 | 100 | 0.6919 | 0.6792 | 0.6025 |
| No log | 1.75 | 200 | 0.6092 | 0.7619 | 0.3357 |
| No log | 2.63 | 300 | 0.5160 | 0.7669 | 0.6353 |
| No log | 3.51 | 400 | 0.9501 | 0.7093 | 0.6352 |
| 0.3526 | 4.39 | 500 | 0.7742 | 0.7794 | 0.6857 |
| 0.3526 | 5.26 | 600 | 0.8757 | 0.7895 | 0.6794 |
| 0.3526 | 6.14 | 700 | 0.6974 | 0.8195 | 0.6897 |
| 0.3526 | 7.02 | 800 | 1.0621 | 0.8221 | 0.6203 |
| 0.3526 | 7.89 | 900 | 1.5238 | 0.7694 | 0.6933 |
| 0.0789 | 8.77 | 1000 | 1.3006 | 0.7820 | 0.6969 |
| 0.0789 | 9.65 | 1100 | 1.1286 | 0.8246 | 0.7083 |
| 0.0789 | 10.53 | 1200 | 0.8404 | 0.8346 | 0.736 |
| 0.0789 | 11.4 | 1300 | 1.1477 | 0.8321 | 0.7100 |
| 0.0789 | 12.28 | 1400 | 1.0099 | 0.8421 | 0.7042 |
| 0.0257 | 13.16 | 1500 | 1.1806 | 0.8371 | 0.7032 |
| 0.0257 | 14.04 | 1600 | 1.1848 | 0.8170 | 0.6332 |
| 0.0257 | 14.91 | 1700 | 1.1102 | 0.8321 | 0.6996 |
| 0.0257 | 15.79 | 1800 | 1.0778 | 0.8271 | 0.6667 |
| 0.0257 | 16.67 | 1900 | 1.2473 | 0.8296 | 0.6634 |
| 0.022 | 17.54 | 2000 | 1.2310 | 0.8221 | 0.7054 |
| 0.022 | 18.42 | 2100 | 1.0758 | 0.8346 | 0.6887 |
| 0.022 | 19.3 | 2200 | 1.4127 | 0.8070 | 0.7220 |
| 0.022 | 20.18 | 2300 | 1.0067 | 0.8246 | 0.6789 |
| 0.022 | 21.05 | 2400 | 1.2720 | 0.8045 | 0.6829 |
| 0.0209 | 21.93 | 2500 | 1.2202 | 0.8296 | 0.6731 |
| 0.0209 | 22.81 | 2600 | 1.3424 | 0.8221 | 0.6468 |
| 0.0209 | 23.68 | 2700 | 1.0766 | 0.8346 | 0.7130 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
