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
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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)_](https://wfhmap.com/). The model is a finetuned version of the [distibert-base-uncased](https://huggingface.co/distilbert/distilbert-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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