rasyosef/amharic-named-entity-recognition

notebooks to finetune Amharic BERT and RoBERTa models for named entity recognition # Amharic Named Entity Recognition The following transformer models were finetuned using the amharic-named-entity-recognition dataset. The *finetuning notebooks* can be found in the `notebooks` folder. The reported precision, recall, and f1 metrics are macro averages. |Model|Size (# params)| Precision | Recall | F1 | | --- | ------------- | --------- |------- | -- | |roberta-base-amharic|110M|**0.74**|**0.80**|**0.78**| |roberta-medium-amharic|42.2M|0.72|0.79|0.75| |bert-medium-amharic|40.5M|0.64|0.73|0.68| |bert-small-amharic|27.8M|0.64|0.72|0.68| |bert-mini-amharic|10.7M|0.60|0.67|0.64| |bert-tiny-amharic|4.18M|0.50|0.59|0.54| |xlm-roberta-base|279M|0.69|0.79|0.73| |afro-xlmr-base|278M|0.71|0.78|0.75| |afro-xlmr-large|560M|0.73|0.80|0.76| |am-roberta|443M|0.67|0.72|0.69| ### Fine-tuned Model A finetuned `bert-medium-amharic` model is available on HuggingFace: rasyosef/bert-medium-amharic-finetuned-ner #### How to use You can use this model directly with a pipeline for token classification: ```python from transformers import pipeline checkpoint = "rasyosef/bert-medium-amharic-finetuned-ner" token_classifier = pipeline("token-classification", model=checkpoint, aggregation_strategy="simple") token_classifier("አትሌት ኃይሌ ገ/ሥላሴ ኒውዮርክ ውስጥ በሚደረገው የተባበሩት መንግሥታት ድርጅት ልዩ የሰላም ስብሰባ ላይ እንዲገኝ ተጋበዘ።") ``` Output: ```python [{'entity_group': 'TTL', 'score': 0.9841112, 'word': 'አትሌት', 'start': 0, 'end': 4}, {'entity_group': 'PER', 'score': 0.99379075, 'word': 'ኃይሌ ገ / ሥላሴ', 'start': 5, 'end': 14}, {'entity_group': 'LOC', 'score': 0.8818362, 'word': 'ኒውዮርክ', 'start': 15, 'end': 20}, {'entity_group': 'ORG', 'score': 0.99056435, 'word': 'የተባበሩት መንግሥታት ድርጅት', 'start': 32, 'end': 50}] ``` ### References - Token classification