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--- |
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language: |
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- en |
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tags: |
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- text-classification |
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- emotion |
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- pytorch |
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license: mit |
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datasets: |
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- emotion |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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--- |
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# DistilBERT-Base-Uncased-Emotion |
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## Model Description |
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`distilbert-base-uncased-emotion` is a specialized model finetuned on a combination of unify-emotion-datasets (https://github.com/sarnthil/unify-emotion-datasets), containing around 250K texts labeled across seven emotion categories: neutral, happy, sad, anger, disgust, surprise, and fear. This model was later adapted to a smaller set of 10K hand-tagged messages from StockTwits. The model is designed to excel at emotion detection in financial social media content such as that found on StockTwits. |
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Model parameters were as follows: sequence length of 64, learning rate of 2e-5, batch size of 128, trained for 8 epochs. For steps on how to use the model for inference, please refer to the accompanying Inference.ipynb notebook. |
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## Training Data |
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The training data was obtained from the Unify Emotion Datasets available at https://github.com/sarnthil/unify-emotion-datasets. |
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## Evaluation Metrics |
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The model was evaluated using the following metrics: |
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- Accuracy |
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- Precision |
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- Recall |
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- F1-score |
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## Research |
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The underlying research for emotion extraction from social media can be found in the paper "EmTract: Extracting Emotions from Social Media". |
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The paper is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3975884. |
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### Research using EmTract |
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[Social Media Emotions and IPO Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4384573) |
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[Investor Emotions and Earnings Announcements](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3626025]) |
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## License |
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This project is licensed under the terms of the MIT license. |
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