Text Classification
Transformers
PyTorch
Safetensors
German
bert
feature-extraction
text-embeddings-inference
Instructions to use hplisiecki/word2affect_german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hplisiecki/word2affect_german with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hplisiecki/word2affect_german")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hplisiecki/word2affect_german") model = AutoModel.from_pretrained("hplisiecki/word2affect_german", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Affective Norms Extrapolation Model for German Language
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## Disclaimer
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This model and other models from the
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Their accuracy on long-form text is unknown.
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## Model Description
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# Affective Norms Extrapolation Model for German Language
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## Disclaimer
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This model and other models from the word2affect line are finetuned to predict emotional load for singular words, not longer utterances.
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Their accuracy on long-form text is unknown.
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## Model Description
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