--- license: mit tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: Climate-TwitterBERT-xmas results: [] --- # Climate-TwitterBERT-xmas This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5380 - Accuracy: 0.772 - Precision: 0.0 - Recall: 0.0 - F1-weighted: 0.6727 - F1: 0.0 ## 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: 0.001 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1-weighted | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:-----------:|:------:| | 0.6568 | 0.11 | 50 | 0.5513 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.7992 | 0.23 | 100 | 0.5829 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 1.0537 | 0.34 | 150 | 0.8565 | 0.228 | 0.228 | 1.0 | 0.0847 | 0.3713 | | 0.874 | 0.46 | 200 | 0.9819 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.8665 | 0.57 | 250 | 0.6095 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.6528 | 0.68 | 300 | 1.2397 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.9204 | 0.8 | 350 | 0.6173 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.6515 | 0.91 | 400 | 0.5916 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.6148 | 1.03 | 450 | 0.6140 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.7454 | 1.14 | 500 | 0.7652 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.6223 | 1.26 | 550 | 0.5375 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.6466 | 1.37 | 600 | 1.0611 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.7892 | 1.48 | 650 | 0.7350 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.602 | 1.6 | 700 | 0.7024 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.8074 | 1.71 | 750 | 1.0565 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.8569 | 1.83 | 800 | 0.6465 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.6455 | 1.94 | 850 | 0.5401 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.6624 | 2.05 | 900 | 0.6494 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.5909 | 2.17 | 950 | 0.5444 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.5943 | 2.28 | 1000 | 0.7197 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.7885 | 2.4 | 1050 | 0.5382 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.6684 | 2.51 | 1100 | 0.5417 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.5723 | 2.63 | 1150 | 0.9149 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.747 | 2.74 | 1200 | 0.6202 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.6384 | 2.85 | 1250 | 0.5374 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.634 | 2.97 | 1300 | 0.6424 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.6256 | 3.08 | 1350 | 0.7462 | 0.228 | 0.228 | 1.0 | 0.0847 | 0.3713 | | 0.6282 | 3.2 | 1400 | 0.5424 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.5496 | 3.31 | 1450 | 0.5402 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.5245 | 3.42 | 1500 | 0.5399 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.5226 | 3.54 | 1550 | 0.5699 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.6251 | 3.65 | 1600 | 0.5496 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.5547 | 3.77 | 1650 | 0.5435 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.532 | 3.88 | 1700 | 0.5381 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | | 0.6029 | 4.0 | 1750 | 0.5380 | 0.772 | 0.0 | 0.0 | 0.6727 | 0.0 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.13.3