Instructions to use mmaguero/multi-wiki-qa-gn-bert-tiny-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmaguero/multi-wiki-qa-gn-bert-tiny-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mmaguero/multi-wiki-qa-gn-bert-tiny-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mmaguero/multi-wiki-qa-gn-bert-tiny-cased") model = AutoModelForQuestionAnswering.from_pretrained("mmaguero/multi-wiki-qa-gn-bert-tiny-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mmaguero/multi-wiki-qa-gn-bert-tiny-cased: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/mmaguero/multi-wiki-qa-gn-bert-tiny-cased/resolve/main/training_args.bin
- Command line
-
hf download hf://mmaguero/multi-wiki-qa-gn-bert-tiny-cased/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mmaguero/multi-wiki-qa-gn-bert-tiny-cased/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- 3a831bfb57e46c65edf6e3796fae97570a63ecc4ef6187e1f36cadfcab93b7f4
- Size of remote file:
- 5.84 kB
- SHA256:
- c3abf6666cde2e59243617451ae6069caa4fa015fe4f64242bbb3b55cab4f9ae
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