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