Instructions to use mmine/testwfh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmine/testwfh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mmine/testwfh")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mmine/testwfh") model = AutoModelForSequenceClassification.from_pretrained("mmine/testwfh") - Notebooks
- Google Colab
- Kaggle
This is an attempt to reproduce the model developed in the paper “Remote Work across Jobs, Companies, and Space” (Hansen, Lambert, Bloom, Davis, Sadun & Taska, 2023). The model is a finetuned version of the distibert-base-uncased model on the binary classfication task of predicting if a fragment of text exhibits the possibility of remote work (=1) or not (=0).
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