--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - tweet_sentiment_multilingual metrics: - accuracy - f1 model-index: - name: scenario-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_ results: [] --- # scenario-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tweet_sentiment_multilingual dataset. It achieves the following results on the evaluation set: - Loss: 3.2943 - Accuracy: 0.5494 - F1: 0.5481 ## 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: 222 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 1.0706 | 1.09 | 500 | 1.0260 | 0.4946 | 0.4746 | | 0.97 | 2.17 | 1000 | 0.9626 | 0.5270 | 0.5111 | | 0.9007 | 3.26 | 1500 | 0.9517 | 0.5490 | 0.5506 | | 0.8292 | 4.35 | 2000 | 0.9865 | 0.5475 | 0.5339 | | 0.7773 | 5.43 | 2500 | 1.0123 | 0.5475 | 0.5388 | | 0.7155 | 6.52 | 3000 | 1.0156 | 0.5536 | 0.5509 | | 0.6707 | 7.61 | 3500 | 1.0641 | 0.5637 | 0.5629 | | 0.6081 | 8.7 | 4000 | 1.1244 | 0.5617 | 0.5614 | | 0.5489 | 9.78 | 4500 | 1.1851 | 0.5475 | 0.5404 | | 0.503 | 10.87 | 5000 | 1.3213 | 0.5586 | 0.5591 | | 0.4602 | 11.96 | 5500 | 1.4164 | 0.5563 | 0.5520 | | 0.4134 | 13.04 | 6000 | 1.4394 | 0.5482 | 0.5414 | | 0.3697 | 14.13 | 6500 | 1.5564 | 0.5529 | 0.5501 | | 0.3256 | 15.22 | 7000 | 1.5412 | 0.5478 | 0.5465 | | 0.3028 | 16.3 | 7500 | 1.6169 | 0.5374 | 0.5353 | | 0.2673 | 17.39 | 8000 | 1.7963 | 0.5436 | 0.5432 | | 0.2501 | 18.48 | 8500 | 1.7266 | 0.5517 | 0.5499 | | 0.2225 | 19.57 | 9000 | 1.9252 | 0.5532 | 0.5472 | | 0.2021 | 20.65 | 9500 | 1.9622 | 0.5563 | 0.5542 | | 0.1875 | 21.74 | 10000 | 1.9973 | 0.5471 | 0.5463 | | 0.1681 | 22.83 | 10500 | 2.1299 | 0.5386 | 0.5307 | | 0.1534 | 23.91 | 11000 | 2.0761 | 0.5463 | 0.5416 | | 0.1427 | 25.0 | 11500 | 2.2814 | 0.5475 | 0.5471 | | 0.1304 | 26.09 | 12000 | 2.4128 | 0.5544 | 0.5451 | | 0.1126 | 27.17 | 12500 | 2.4318 | 0.5370 | 0.5327 | | 0.1169 | 28.26 | 13000 | 2.5110 | 0.5451 | 0.5432 | | 0.1044 | 29.35 | 13500 | 2.5768 | 0.5467 | 0.5432 | | 0.1011 | 30.43 | 14000 | 2.6120 | 0.5486 | 0.5428 | | 0.0982 | 31.52 | 14500 | 2.5795 | 0.5544 | 0.5541 | | 0.0854 | 32.61 | 15000 | 2.7525 | 0.5482 | 0.5497 | | 0.0853 | 33.7 | 15500 | 2.7322 | 0.5575 | 0.5557 | | 0.0851 | 34.78 | 16000 | 2.7708 | 0.5417 | 0.5375 | | 0.0726 | 35.87 | 16500 | 2.8363 | 0.5451 | 0.5417 | | 0.0706 | 36.96 | 17000 | 2.8634 | 0.5505 | 0.5494 | | 0.0653 | 38.04 | 17500 | 2.9653 | 0.5444 | 0.5434 | | 0.0669 | 39.13 | 18000 | 3.0624 | 0.5432 | 0.5417 | | 0.0585 | 40.22 | 18500 | 3.1669 | 0.5432 | 0.5392 | | 0.059 | 41.3 | 19000 | 3.0692 | 0.5548 | 0.5544 | | 0.048 | 42.39 | 19500 | 3.2014 | 0.5494 | 0.5482 | | 0.0479 | 43.48 | 20000 | 3.2452 | 0.5428 | 0.5409 | | 0.052 | 44.57 | 20500 | 3.2338 | 0.5478 | 0.5476 | | 0.0477 | 45.65 | 21000 | 3.2556 | 0.5444 | 0.5424 | | 0.0395 | 46.74 | 21500 | 3.2952 | 0.5444 | 0.5420 | | 0.0477 | 47.83 | 22000 | 3.2726 | 0.5509 | 0.5500 | | 0.0408 | 48.91 | 22500 | 3.2894 | 0.5471 | 0.5457 | | 0.0407 | 50.0 | 23000 | 3.2943 | 0.5494 | 0.5481 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3