Instructions to use daotc2/xlmr-qa-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daotc2/xlmr-qa-large 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="daotc2/xlmr-qa-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("daotc2/xlmr-qa-large") model = AutoModelForQuestionAnswering.from_pretrained("daotc2/xlmr-qa-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from daotc2/xlmr-qa-large: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/daotc2/xlmr-qa-large/resolve/main/tokenizer.json
- Command line
-
hf download hf://daotc2/xlmr-qa-large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/daotc2/xlmr-qa-large/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- cf8be75a5d2e1cb169fac78927a1ef273055cac28a2b7b1a789145ac8f12ce0c
- Size of remote file:
- 17.1 MB
- SHA256:
- a497fd22e7c0f0994ae56f40e5ec8ef7c8a9518b264d55095021794d694aa6cb
路
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