ProcessOutcomePaper/ProcessOutcome

Model Card for Process and Outcome Framing Classification

Model Description

This is the ProcessOutcomeBERT language model, a language model trained to classify texts in process and outcome framing for enviormental posts.

The model fine-tuned the BERT model on a dataset of 2,000 environmental posts

How to Get Started With the Model

You can use this model with Transformers pipeline for process and outcome framine classification:

#Importing Required Classes and Functions
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline


#Loading the Pre-trained Tokenizer
tokenizer = AutoTokenizer.from_pretrained("ProcessOutcomePaper/ProcessOutcome")

#Loading the Model
model = AutoModelForSequenceClassification.from_pretrained("ProcessOutcomePaper/ProcessOutcome")


# Creating the Process and Outcome framine Classification Pipeline
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)

# Using the Pipeline to Classify Text
# detail can be found: https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline
# pipe("input the text here"). Example as below:

print(pipe("We want to share an important milestone on our sustainability journey. We’ve achieved 100% use of recycled boxes for all product deliveries, reducing packaging waste by 20%."))
print(pipe("To protect the environment, we have optimized packaging using recycle materials and invest in renewable energy."))