Instructions to use MistahCase/distilroberta-base-testingSB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MistahCase/distilroberta-base-testingSB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="MistahCase/distilroberta-base-testingSB")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("MistahCase/distilroberta-base-testingSB") model = AutoModelForMaskedLM.from_pretrained("MistahCase/distilroberta-base-testingSB") - Notebooks
- Google Colab
- Kaggle
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Model description
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Customer-specific model used to embed asset management work orders in Danish
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## Intended uses & limitations
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Customer-specific and trained for unsupervised categorization tasks
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## Training and evaluation data
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