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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- f1 |
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- accuracy |
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base_model: |
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- google-t5/t5-base |
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library_name: transformers |
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--- |
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# Computational Analysis of Communicative Acts for Understanding Crisis News Comment Discourses |
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The official trained models for **"Computational Analysis of Communicative Acts for Understanding Crisis News Comment Discourses"**. |
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This model is based on **T5-base** and uses the **Compacter** ([Compacter: Efficient Low-Rank Adaptation for Transformer Models](https://arxiv.org/abs/2106.04647)) architecture. It has been fine-tuned on our **crisis narratives dataset**. |
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--- |
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### Model Information |
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- **Architecture:** T5-base with Compacter |
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- **Task:** Single-label classification for communicative act actions |
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- **Classes:** |
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- `informing statement` |
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- `challenge` |
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- `rejection` |
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- `appreciation` |
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- `request` |
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- `question` |
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- `acceptance` |
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- `apology` |
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--- |
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### How to Use the Model |
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To use this model, you will need the original code from our paper, available here: |
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[Acts in Crisis Narratives - GitHub Repository](https://github.com/Aalto-CRAI-CIS/Acts-in-crisis-narratives/tree/main/few_shot_learning/AdapterModel) |
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#### Steps to Load and Use the Fine-Tuned Model: |
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1. Add your test task method to `seq2seq/data/task.py`, similar to other task methods. |
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2. Modify `adapter_inference.sh` to include your test task's information and this model's name, and then run it. |
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```bash |
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--model_name_or_path CrisisNarratives/adapter-8classes-single_label |
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``` |
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For detailed instructions, refer to the GitHub repository linked above. |
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--- |
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### Citation |
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If you use this model in your work, please cite: |
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##### TO BE ADDED. |
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### Questions or Feedback? |
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For questions or feedback, please reach out via our [contact form](mailto:[email protected]). |
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