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:
- e5c2f09dfa14d0514fca1e69dae6d0da84ca944a75326387fd1121c90fd9fc3f
- Size of remote file:
- 1.11 GB
- SHA256:
- 5210ff7500343b36b6c9e4211dd09db87ba59ecba318d4f046b09a87b9042540
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