| language: | |
| - en | |
| license: mit | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| widget: | |
| - text: Forest fire near La Ronge Sask. Canada | |
| example_title: 有灾情 | |
| - text: Summer is lovely | |
| example_title: 无灾情 | |
| base_model: roberta-large | |
| model-index: | |
| - name: roberta-large-finetuned-disaster | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # roberta-large-finetuned-disaster | |
| This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [Disaster Tweets](https://www.kaggle.com/competitions/nlp-getting-started/data). | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3668 | |
| - Accuracy: 0.8399 | |
| - F1: 0.8396 | |
| ## Model description | |
| The model is a fine-tuned version on the disaster dataset on Kaggle. You can enter the following statement to see if the label changes: | |
| ```txt | |
| Forest fire near La Ronge Sask. Canada | |
| Just happened a terrible car crash | |
| What's up man? | |
| Summer is lovely | |
| ``` | |
| ## 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: 2e-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: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | 0.446 | 1.0 | 226 | 0.3657 | 0.8583 | 0.8580 | | |
| | 0.3295 | 2.0 | 452 | 0.3668 | 0.8399 | 0.8396 | | |
| ### Framework versions | |
| - Transformers 4.26.1 | |
| - Pytorch 1.13.0 | |
| - Datasets 2.1.0 | |
| - Tokenizers 0.13.2 |