readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: crisis_emotion_roberta
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  # crisis_emotion_roberta
  
  This emotion classification model is a fine-tuned version of [finiteautomata/bertweet-base-sentiment-analysis](https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis) on a dataset of 9,300 tweets in the Flint Water Crisis (Wu, Wong, Zhao, & Liu, 2021). It achieves the following results on the testing set:
  0.75 accuracy, 0.74 weighted accuracy, and 0.68 macro accuracy. 
  
  ## Classify the primary emotion of a crisis tweet into one of the following 6 categories (The F-1 score for each emotion):
  0. Anger (0.83)
  1. Sadness (0.67)
  2. Joy (0.69)
  3. Sympathy (0.80)
  4. Sarcasm (0.44)
  5. Neutral (0.64)
  
  To cite our work: 
  Wu, J., Wong, C.-W., Zhao, X., & Liu, X. (2021). Toward effective automated content analysis via crowdsourcing. Paper presented at the IEEE International Conference on Multimedia and Expo (ICME). https://doi.org/10.1109/ICME51207.2021.9428220
  
  ## Intended uses & limitations
  
  For classifying the emotion of English tweets during crises & disasters
  
  ## Training and evaluation data
  
  Dataset: 9,300 tweets in the Flint water crisis. 
  Each tweet was labeled by trained & qualified crowdsourcing workers for 3-5 times.
  For detail, see our IEEE ICME paper - Wu, Wong, Zhao, & Liu, 2021. (https://arxiv.org/pdf/2101.04615.pdf)
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.7558        | 1.0   | 349  | 0.8849          | 0.6839   |
  | 0.7716        | 2.0   | 698  | 0.8137          | 0.7306   |
  | 1.0935        | 3.0   | 1047 | 0.8435          | 0.7333   |
  | 0.4497        | 4.0   | 1396 | 0.9084          | 0.7371   |
  | 0.3247        | 5.0   | 1745 | 1.0200          | 0.7355   |
  | 0.0225        | 6.0   | 2094 | 1.1517          | 0.7344   |
  | 0.2034        | 7.0   | 2443 | 1.2812          | 0.7333   |
  | 0.0224        | 8.0   | 2792 | 1.4054          | 0.7258   |
  | 0.008         | 9.0   | 3141 | 1.4090          | 0.7242   |
  | 0.0067        | 10.0  | 3490 | 1.4884          | 0.7204   |
  | 0.4066        | 11.0  | 3839 | 1.5450          | 0.7220   |
  | 0.0033        | 12.0  | 4188 | 1.6056          | 0.7247   |
  | 0.003         | 13.0  | 4537 | 1.6327          | 0.7247   |
  | 0.0037        | 14.0  | 4886 | 1.6871          | 0.7285   |
  | 0.0025        | 15.0  | 5235 | 1.6898          | 0.7274   |
  
  
  ### Framework versions
  
  - Transformers 4.23.0.dev0
  - Pytorch 1.13.0.dev20220917+cu117
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  