Instructions to use intanm/mdeberta-idkmrc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intanm/mdeberta-idkmrc with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="intanm/mdeberta-idkmrc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("intanm/mdeberta-idkmrc") model = AutoModelForQuestionAnswering.from_pretrained("intanm/mdeberta-idkmrc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from intanm/mdeberta-idkmrc: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/intanm/mdeberta-idkmrc/resolve/main/tokenizer.json
- Command line
-
hf download hf://intanm/mdeberta-idkmrc/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/intanm/mdeberta-idkmrc/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 8a8192d62d29b0a558708d7268bf3c0909c65e0423da2199539fe1e0a97ec75a
- Size of remote file:
- 16.3 MB
- SHA256:
- 2d892e4e14c0e6c54c672657a2bfa5c6a5a38de3f6699d93dc568142930ec88b
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