Instructions to use cx-olquinjica/angXLMR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cx-olquinjica/angXLMR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cx-olquinjica/angXLMR")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cx-olquinjica/angXLMR") model = AutoModelForMaskedLM.from_pretrained("cx-olquinjica/angXLMR") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ed11a1a1034b49c483cafcbdc17f10403f15a9b5349166e2856db400f770e02
- Size of remote file:
- 1.11 GB
- SHA256:
- 1317c463d093afa140225ebfeb747433a191d97ce4f1198eac2e4b721c6c8127
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