metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - f1
  - accuracy
widget:
  - text: >-
      A combined 20 million people per year die of smoking and hunger,  so
      authorities can't seem to feed people and they allow you to buy cigarettes
      but we are facing another lockdown for a virus that has a 99.5% survival
      rate!!! THINK PEOPLE. LOOK AT IT LOGICALLY WITH YOUR OWN EYES.
  - text: >-
      Scientists do not agree on the consequences of climate change, nor is
      there any consensus on that subject. The predictions on that from are just
      ascientific speculation. Bring on the warming."
  - text: >-
      If Tam is our "top doctor"....I am going back to leaches and voodoo...just
      as much science in that as the crap she spouts
  - text: "Can she skip school by herself and sit infront of parliament? \r\n Fake emotions and just a good actor."
  - text: my dad had huge ones..so they may be real..
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
  - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: metric
            value: 0.688144336139226
            name: Metric
license: mit
language:
  - en

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 SetFit (SetFit: Efficient Few-Shot Learning Without Prompts) and uses the sentence-transformers/paraphrase-mpnet-base-v2 pretrained model. It has been fine-tuned on our crisis narratives dataset.

Model Information

Architecture: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
Task: Multi-label classification for communicative act actions
Classes:
- informing statement
- challenge
- accusation
- rejection
- appreciation
- request
- question
- acceptance
- apology

How to Use the Model

You can find the code to fine-tune this model and detailed instructions in the following GitHub repository:
Acts in Crisis Narratives - SetFit Fine-Tuning Notebook

Steps to Load and Use the Model:

Install the SetFit library:
```
pip install setfit
```

Load the model and run inference:

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("CrisisNarratives/setfit-9classes-multi_label")

# Run inference
preds = model("I'm sorry.")

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.