Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use LKriesch/TwinTransitionMapper_AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use LKriesch/TwinTransitionMapper_AI with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("LKriesch/TwinTransitionMapper_AI") - sentence-transformers
How to use LKriesch/TwinTransitionMapper_AI with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LKriesch/TwinTransitionMapper_AI") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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README.md
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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2. Training a classification head with features from the fine-tuned Sentence Transformer.
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The model is designed to predict the AI capabilities of German companies based on their website texts. It is intended to be used in conjunction with the Twin_Transition_Mapper_Green 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: [LINK TO WORKING PAPER]
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### Model Description
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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2. Training a classification head with features from the fine-tuned Sentence Transformer.
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The model is designed to predict the AI capabilities of German companies based on their website texts. It is intended to be used in conjunction with the [Twin_Transition_Mapper_Green model] (https://huggingface.co/LKriesch/TwinTransitionMapper_Green) 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: [LINK TO WORKING PAPER]
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### Model Description
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