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 pytorch_model.bin from tamhuynh27/deberta-base-finetuned-plot: direct link, hf CLI and curl.
- Browser
- Download file 554 MB
-
https://huggingface.co/tamhuynh27/deberta-base-finetuned-plot/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tamhuynh27/deberta-base-finetuned-plot/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tamhuynh27/deberta-base-finetuned-plot/resolve/main/pytorch_model.bin
554 MB
- Xet hash:
- d3d1231c2473f10913c2bbb15fb8fd23dba364142ca84a1a055bee0e465c53ef
- Size of remote file:
- 554 MB
- SHA256:
- 6a08b11c2bb115fb9ac0e192eafa980ced9b480cf6f2edd11754ddaffaf9d7e6
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.