Instructions to use TimSchopf/nlp_taxonomy_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TimSchopf/nlp_taxonomy_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TimSchopf/nlp_taxonomy_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TimSchopf/nlp_taxonomy_classifier") model = AutoModelForSequenceClassification.from_pretrained("TimSchopf/nlp_taxonomy_classifier") - Notebooks
- Google Colab
- Kaggle
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README.md
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📄 Paper: [Exploring the Landscape of Natural Language Processing Research (RANLP 2023)](https://aclanthology.org/2023.ranlp-1.111)
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💾 Data: [https://huggingface.co/datasets/TimSchopf/nlp_taxonomy_data](https://huggingface.co/datasets/TimSchopf/nlp_taxonomy_data)
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📄 Paper: [Exploring the Landscape of Natural Language Processing Research (RANLP 2023)](https://aclanthology.org/2023.ranlp-1.111)
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💻 GitHub: [https://github.com/sebischair/Exploring-NLP-Research](https://github.com/sebischair/Exploring-NLP-Research)
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💾 Data: [https://huggingface.co/datasets/TimSchopf/nlp_taxonomy_data](https://huggingface.co/datasets/TimSchopf/nlp_taxonomy_data)
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