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