Instructions to use evangeliazve/mpnet-base-articles-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use evangeliazve/mpnet-base-articles-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="evangeliazve/mpnet-base-articles-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("evangeliazve/mpnet-base-articles-ner") model = AutoModelForTokenClassification.from_pretrained("evangeliazve/mpnet-base-articles-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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- generated_from_trainer
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metrics:
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- f1
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model-index:
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- name: mpnet-base-articles-ner
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results: []
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- generated_from_trainer
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metrics:
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base_model: microsoft/mpnet-base
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model-index:
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- name: mpnet-base-articles-ner
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results: []
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