Instructions to use ICTuniverse/XLM-R-large-qa-one-shot-viwiki with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ICTuniverse/XLM-R-large-qa-one-shot-viwiki 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="ICTuniverse/XLM-R-large-qa-one-shot-viwiki")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ICTuniverse/XLM-R-large-qa-one-shot-viwiki") model = AutoModelForQuestionAnswering.from_pretrained("ICTuniverse/XLM-R-large-qa-one-shot-viwiki", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ICTuniverse/XLM-R-large-qa-one-shot-viwiki: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/ICTuniverse/XLM-R-large-qa-one-shot-viwiki/resolve/main/tokenizer.json
- Command line
-
hf download hf://ICTuniverse/XLM-R-large-qa-one-shot-viwiki/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ICTuniverse/XLM-R-large-qa-one-shot-viwiki/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- b036c0e8fb357ddf7d46dcd7533069a57249ed0b93e3273bedd3d6761f4b3696
- Size of remote file:
- 17.1 MB
- SHA256:
- f70dfc5504ec483522f1da000b5ebfc77a0d6284a5351821b551045503c7c6e6
路
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