ProcessOutcomePaper/ProcessOutcome

1---2license: mit3language:4- en5base_model:6- google-bert/bert-base-uncased7pipeline_tag: text-classification8library_name: transformers9---10 11# Model Card for Process and Outcome Framing Classification12## Model Description13This is the ProcessOutcomeBERT language model, a language model trained to classify texts in process and outcome framing for enviormental posts. 14 15The model fine-tuned the BERT model on a dataset of 2,000 environmental posts16 17## How to Get Started With the Model18You can use this model with Transformers pipeline for process and outcome framine classification:19 20```python 21#Importing Required Classes and Functions22from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline23 24 25#Loading the Pre-trained Tokenizer26tokenizer = AutoTokenizer.from_pretrained("ProcessOutcomePaper/ProcessOutcome")27 28#Loading the Model29model = AutoModelForSequenceClassification.from_pretrained("ProcessOutcomePaper/ProcessOutcome")30 31 32# Creating the Process and Outcome framine Classification Pipeline33pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)34 35# Using the Pipeline to Classify Text36# detail can be found: https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline37# pipe("input the text here"). Example as below:38 39print(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%."))40print(pipe("To protect the environment, we have optimized packaging using recycle materials and invest in renewable energy."))41```42