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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## How to use the fine-tuned model
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A machine readable version of the NLP taxonomy is available in our code repository as an OWL file: [https://github.com/sebischair/Exploring-NLP-Research/blob/main/NLP-Taxonomy.owl](https://github.com/sebischair/Exploring-NLP-Research/blob/main/NLP-Taxonomy.owl)
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For our work on [NLP-KG](https://aclanthology.org/2024.acl-demos.13), we extended this taxonomy to a large hierarchy of fields of study in NLP and made it available in a machine readable format as an OWL file at: [https://github.com/NLP-Knowledge-Graph/NLP-KG-WebApp](https://github.com/NLP-Knowledge-Graph/NLP-KG-WebApp)
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## How to use the fine-tuned model
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