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scenario-non-kd-from-post-finetune-div-2-data-indolem_sentiment-model-haryoaw-sc
This model is a fine-tuned version of haryoaw/scenario-normal-finetune-clf-data-indolem_sentiment-model-xlm-roberta-base on the indolem_sentiment dataset. It achieves the following results on the evaluation set:
- Loss: 1.0411
- Accuracy: 0.8647
- F1: 0.7823
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 | 0.6692 | 0.7594 | 0.6962 |
No log | 1.75 | 200 | 0.3987 | 0.8195 | 0.7429 |
No log | 2.63 | 300 | 0.7587 | 0.8170 | 0.7591 |
No log | 3.51 | 400 | 0.5380 | 0.8521 | 0.7592 |
0.2364 | 4.39 | 500 | 0.9505 | 0.8396 | 0.7576 |
0.2364 | 5.26 | 600 | 0.7528 | 0.8571 | 0.7692 |
0.2364 | 6.14 | 700 | 0.7176 | 0.8571 | 0.7511 |
0.2364 | 7.02 | 800 | 0.6990 | 0.8446 | 0.7395 |
0.2364 | 7.89 | 900 | 0.6388 | 0.8672 | 0.7745 |
0.0693 | 8.77 | 1000 | 0.5099 | 0.8596 | 0.7647 |
0.0693 | 9.65 | 1100 | 0.8001 | 0.8471 | 0.7608 |
0.0693 | 10.53 | 1200 | 0.6682 | 0.8847 | 0.8115 |
0.0693 | 11.4 | 1300 | 0.8207 | 0.8797 | 0.8140 |
0.0693 | 12.28 | 1400 | 0.9404 | 0.8596 | 0.7522 |
0.0412 | 13.16 | 1500 | 0.7976 | 0.8596 | 0.7586 |
0.0412 | 14.04 | 1600 | 0.8785 | 0.8672 | 0.7854 |
0.0412 | 14.91 | 1700 | 0.8712 | 0.8571 | 0.7532 |
0.0412 | 15.79 | 1800 | 1.0122 | 0.8596 | 0.7477 |
0.0412 | 16.67 | 1900 | 0.7452 | 0.8747 | 0.7899 |
0.0303 | 17.54 | 2000 | 1.0122 | 0.8622 | 0.7791 |
0.0303 | 18.42 | 2100 | 0.8764 | 0.8647 | 0.7611 |
0.0303 | 19.3 | 2200 | 0.8529 | 0.8697 | 0.7570 |
0.0303 | 20.18 | 2300 | 0.8582 | 0.8847 | 0.8034 |
0.0303 | 21.05 | 2400 | 1.0437 | 0.8571 | 0.7782 |
0.0204 | 21.93 | 2500 | 0.9941 | 0.8647 | 0.7632 |
0.0204 | 22.81 | 2600 | 1.0960 | 0.8546 | 0.7411 |
0.0204 | 23.68 | 2700 | 0.7326 | 0.8697 | 0.7615 |
0.0204 | 24.56 | 2800 | 1.0411 | 0.8647 | 0.7823 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3