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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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