Instructions to use tamhuynh27/deberta-base-finetuned-plot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tamhuynh27/deberta-base-finetuned-plot 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="tamhuynh27/deberta-base-finetuned-plot")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("tamhuynh27/deberta-base-finetuned-plot") model = AutoModelForQuestionAnswering.from_pretrained("tamhuynh27/deberta-base-finetuned-plot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tamhuynh27/deberta-base-finetuned-plot: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/tamhuynh27/deberta-base-finetuned-plot/resolve/main/training_args.bin
- Command line
-
hf download hf://tamhuynh27/deberta-base-finetuned-plot/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tamhuynh27/deberta-base-finetuned-plot/resolve/main/training_args.bin
3.9 kB
- Xet hash:
- feb840438d83eb056856d22389b528608c267d909c4aabbca5b4752993ed49cf
- Size of remote file:
- 3.9 kB
- SHA256:
- 77f8ac84897a99c82cf0ed597a7178048f2ee3123cc3310ae80ee9b8f5c12678
路
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