Instructions to use subhasisj/xlm-roberta-base-squad-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhasisj/xlm-roberta-base-squad-32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="subhasisj/xlm-roberta-base-squad-32")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("subhasisj/xlm-roberta-base-squad-32") model = AutoModelForQuestionAnswering.from_pretrained("subhasisj/xlm-roberta-base-squad-32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from subhasisj/xlm-roberta-base-squad-32: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/subhasisj/xlm-roberta-base-squad-32/resolve/main/training_args.bin
- Command line
-
hf download hf://subhasisj/xlm-roberta-base-squad-32/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/subhasisj/xlm-roberta-base-squad-32/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 4dc036487a95858a6cfa62e931ada0ddb0aa626b1c80c9ba97eaef07a01112bb
- Size of remote file:
- 3.06 kB
- SHA256:
- 84348b41a2bcb34f97d8b8a6ccc501391974f46ebb4c1e93a942cea6863e5ce4
路
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