Instructions to use mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-swahili with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-swahili with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-swahili")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-swahili") model = AutoModelForTokenClassification.from_pretrained("mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-swahili", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5056047d3d508294342b4969d93858153536ef835c5551120fb5572e2bc58b1b
- Size of remote file:
- 1.58 kB
- SHA256:
- 022c4df669a59f60ab9a58a25460a7029d881ec69a9936a19706f6583311471e
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