LKriesch/TwinTransitionMapper_Green

TwinTransitionMapper_Green

This repository contains the model for our paper entitled Not all twins are identical: the digital layer of “twin” transition market applications which is under review in Regional Studies (https://www.tandfonline.com/journals/cres20).

This is a SetFit model that can be used for Text Classification. This SetFit model uses intfloat/multilingual-e5-large as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained on paragraphs from German company websites using an efficient few-shot learning technique that involves:

1. 1.Fine-tuning a Sentence Transformer with contrastive learning.
2. 2.Training a classification head with features from the fine-tuned Sentence Transformer.

The model is designed to predict the clean technology capabilities of German companies based on their website texts. It is intended to be used in conjunction with the TwinTransitionMapper_AI model to identify companies contributing to the twin transition in Germany. For detailed information on the fine-tuning process and the results of these models, please refer to the paper.

Model Description

• —Model Type: SetFit <!-- - Sentence Transformer: Unknown -->
• —Classification head: a LogisticRegression instance
• —Maximum Sequence Length: 512 tokens <!-- - Number of Classes: Unknown --> <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

• —Repository: SetFit on GitHub
• —Paper: Efficient Few-Shot Learning Without Prompts
• —Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("LKriesch/TwinTransitionMapper_Green")
# Run inference
preds = model("I loved the spiderman movie!")

<!--

Downstream Use

List how someone could finetune this model on their own dataset. -->

<!--

Out-of-Scope Use

List how the model may foreseeably be misused and address what users ought not to do with the model. -->

<!--

Bias, Risks and Limitations

What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. -->

<!--

Recommendations

What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. -->

Training Details

Framework Versions

• —Python: 3.9.19
• —SetFit: 1.0.3
• —Sentence Transformers: 3.0.1
• —Transformers: 4.44.0
• —PyTorch: 2.4.0+cu124
• —Datasets: 2.16.1
• —Tokenizers: 0.19.1

Citation

BibTeX

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}

<!--

Glossary

Clearly define terms in order to be accessible across audiences. -->

<!--

Model Card Authors

Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. -->

<!--

Model Card Contact

Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->