Instructions to use emanjavacas/MacBERTh-metric-wsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emanjavacas/MacBERTh-metric-wsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="emanjavacas/MacBERTh-metric-wsd")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("emanjavacas/MacBERTh-metric-wsd") model = AutoModel.from_pretrained("emanjavacas/MacBERTh-metric-wsd") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
This is version of emanjavacas/MacBERTh that has been fine-tuned on data from Oxford English Dictionary (OED) in order to be good at Word Sense Disambiguation (WSD). See https://aclanthology.org/2022.nlp4dh-1.16/ for an explanation of the underlying approach.
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