Instructions to use badokorach/xlm-roberta-base-finetuned-mlqa-Mixed-AGRIC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use badokorach/xlm-roberta-base-finetuned-mlqa-Mixed-AGRIC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="badokorach/xlm-roberta-base-finetuned-mlqa-Mixed-AGRIC")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("badokorach/xlm-roberta-base-finetuned-mlqa-Mixed-AGRIC") model = AutoModelForQuestionAnswering.from_pretrained("badokorach/xlm-roberta-base-finetuned-mlqa-Mixed-AGRIC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6b540031f1d34b69fc35192db805789ada1baa241c004d81baa651f0c30256a
- Size of remote file:
- 1.11 GB
- SHA256:
- dca073d9967a4237cd618a0d0a24f657b7ceb015fcddd6ce10b762dca26a76c5
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