Question Answering
Transformers
Safetensors
Vietnamese
qatc
SemViQA
fact-checking
information-retrieval
Instructions to use SemViQA/qatc-vimrc-isedsc01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SemViQA/qatc-vimrc-isedsc01 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="SemViQA/qatc-vimrc-isedsc01")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForQuestionAnswering model = AutoModelForQuestionAnswering.from_pretrained("SemViQA/qatc-vimrc-isedsc01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SemViQA/qatc-vimrc-isedsc01: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/SemViQA/qatc-vimrc-isedsc01/resolve/main/tokenizer.json
- Command line
-
hf download hf://SemViQA/qatc-vimrc-isedsc01/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SemViQA/qatc-vimrc-isedsc01/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- fd8b244b613f7189b1547758d849817b284f0f3ae52113a915789f927ee4b164
- Size of remote file:
- 17.1 MB
- SHA256:
- 3a56def25aa40facc030ea8b0b87f3688e4b3c39eb8b45d5702b3a1300fe2a20
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