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
| f1 = 0.8893709327548807 | |
| loss = 0.22877022493675644 | |
| precision = 0.8674188998589563 | |
| recall = 0.9124629080118695 | |
| report = precision recall f1-score support | |
| DATE 0.74 0.90 0.81 80 | |
| LOC 0.92 0.92 0.92 303 | |
| ORG 0.69 0.79 0.74 86 | |
| PER 0.94 0.95 0.95 205 | |
| micro avg 0.87 0.91 0.89 674 | |
| macro avg 0.82 0.89 0.85 674 | |
| weighted avg 0.87 0.91 0.89 674 | |