Discourse Classification

Description

Discourse_classification is a Longformer model fine-tuned to identify different discourse elements in a student's writing. The model is fine-tuned for NER Token Classification and uses the PERSUADE dataset for training.

Paper - Identifying discourse elements in writing by fine-tuning BERT, LongFormer, and GPT-2 models for NER Token Classification

All Discourse Elements (NER Token Classifiers)

First, the model divides the corpus into distinct discourse elements and then the elements are classified as one of the following...

• Lead: an introduction that begins with a statistic, a quotation, a description, or some other device to grab the reader’s attention and point toward the thesis
• Position: an opinion or conclusion on the main question
• Claim: a claim that supports the position
• CounterClaim: a claim that refutes another claim or gives an opposing reason to the position
• Rebuttal: a claim that refutes a counterclaim
• Evidence: ideas or examples that support claims, counterclaims, or rebuttals
• Concluding Statment: a concluding statement that restates the claims

Example

Run in browser

1. Go to website https://huggingface.co/brad1141/Longformer_v5
2. Under Hosted inference API, paste the corpus that you wish to evaluate

Run on computer

following code also available in the runModel.ipynb file

from transformers import pipeline

model_checkpoint = "brad1141/Longformer_v5"
token_classifier = pipeline(
    "token-classification", model=model_checkpoint, aggregation_strategy="simple"
)

# code for evaluating a single text
predicts = token_classifier("YOUR TEXT HERE")
for p in predicts:
    print(p)

Kaggle Submission

To evaluate your texts and create a csv file in Kaggle Submission file format, run the file kaggleRun.ipynb with your own text folder