Instructions to use InriaValda/scibert_base_ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InriaValda/scibert_base_ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="InriaValda/scibert_base_ft")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("InriaValda/scibert_base_ft") model = AutoModelForSequenceClassification.from_pretrained("InriaValda/scibert_base_ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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pipeline_tag: text-classification
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- text: "Dijkstra's algorithm is an algorithm for finding the
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example_title: "Djikstra"
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- text: "Proposition 1. The sum of the differential weight d on the Dowker complex D(X, Y, R) is the number of elements of Y ."
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example_title: "Proposition"
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pipeline_tag: text-classification
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widget:
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- text: "Dijkstra's algorithm is an algorithm for finding the paths between nodes in a weighted graph"
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example_title: "Djikstra"
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- text: "Proposition 1. The sum of the differential weight d on the Dowker complex D(X, Y, R) is the number of elements of Y ."
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example_title: "Proposition"
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