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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The model is fine-tuned on a weakly labeled dataset of 178,521 scientific papers from the ACL Anthology, the arXiv cs.CL domain, and Scopus.
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Prior to fine-tuning, the model is initialized with weights from [allenai/specter2](https://huggingface.co/allenai/specter2).
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Paper: [Exploring the Landscape of Natural Language Processing Research (RANLP 2023)](
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Code: https://github.com/sebischair/Exploring-NLP-Research
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<a name="#nlp-taxonomy"/></a>
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The model is fine-tuned on a weakly labeled dataset of 178,521 scientific papers from the ACL Anthology, the arXiv cs.CL domain, and Scopus.
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Prior to fine-tuning, the model is initialized with weights from [allenai/specter2](https://huggingface.co/allenai/specter2).
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📄 Paper: [Exploring the Landscape of Natural Language Processing Research (RANLP 2023)](https://arxiv.org/abs/2307.10652).
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💻 Code: https://github.com/sebischair/Exploring-NLP-Research
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<a name="#nlp-taxonomy"/></a>
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