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CrisisNarratives/
adapter-13classes-multi_label

Name: adapter-13classes-multi_label
Availability: InStock

text-generation-inference

endpoints_compatible

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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) 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
- evaluation
- proposal
- denial
- admission

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

Steps to Load and Use the Fine-Tuned Model:

Add your test task method to seq2seq/data/task.py, similar to other task methods.
Modify adapter_inference.sh to include your test task's information and this model's name, and then run it.

--model_name_or_path CrisisNarratives/adapter-13classes-multi_label

For detailed instructions, refer to the GitHub repository linked above.

Citation

If you use this model in your work, please cite:

Paakki, H., Ghorbanpour, F. (2025). Computational Analysis of Communicative Acts for Understanding Crisis News Comment Discourses. In: Aiello, L.M., Chakraborty, T., Gaito, S. (eds) Social Networks Analysis and Mining. ASONAM 2024. Lecture Notes in Computer Science, vol 15212. Springer, Cham. https://doi.org/10.1007/978-3-031-78538-2_20

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