Instructions to use WaRKiD/bert-large-uncased-whole-word-masking-finetuned-intel-oneapi-llm-dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WaRKiD/bert-large-uncased-whole-word-masking-finetuned-intel-oneapi-llm-dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="WaRKiD/bert-large-uncased-whole-word-masking-finetuned-intel-oneapi-llm-dataset")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("WaRKiD/bert-large-uncased-whole-word-masking-finetuned-intel-oneapi-llm-dataset") model = AutoModelForQuestionAnswering.from_pretrained("WaRKiD/bert-large-uncased-whole-word-masking-finetuned-intel-oneapi-llm-dataset") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 456a1ac63f31f56e706853400ea3b7877b3b57ae427aac47c8e6b70919a438d4
- Size of remote file:
- 1.34 GB
- SHA256:
- 5f9cfe2644406286e98f901ed2dcc9529d3a242671995fb6fb80d7a377481a00
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