Instructions to use aravind-812/roberta-train-json with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aravind-812/roberta-train-json 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="aravind-812/roberta-train-json")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("aravind-812/roberta-train-json") model = AutoModelForQuestionAnswering.from_pretrained("aravind-812/roberta-train-json", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from aravind-812/roberta-train-json: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/aravind-812/roberta-train-json/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://aravind-812/roberta-train-json/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/aravind-812/roberta-train-json/resolve/main/flax_model.msgpack
1.42 GB
- Xet hash:
- 91a8f0dc0cdbff9d7c67f3abb731e3e201dfd292d8d5e50355141cc46442d2af
- Size of remote file:
- 1.42 GB
- SHA256:
- 2244f3818ba8fac43eb95405ecb1da1c26c818b3dcaad7cc22145459b6c20804
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