Instructions to use vbius01/est-roberta-ud-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vbius01/est-roberta-ud-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vbius01/est-roberta-ud-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vbius01/est-roberta-ud-ner") model = AutoModelForTokenClassification.from_pretrained("vbius01/est-roberta-ud-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Verified signature**Model description** est-roberta-ud-ner is an [Est-RoBERTa](https://huggingface.co/EMBEDDIA/est-roberta) based model fine-tuned for named entity recognition on the [EDT](https://github.com/UniversalDependencies/UD_Estonian-EDT) and [EWT](https://github.com/UniversalDependencies/UD_Estonian-EWT) datasets. **How to use** You can use this model with Transformers pipeline for NER. ``` from transformers import pipeline ner = pipeline("ner", model="vbius01/est-roberta-ud-ner") text = "Eesti kuulub erinevalt Lätist ja Leedust kahtlemata Põhjamaade kultuuriruumi." results = ner(text) print(results) ``` ``` [{'entity': 'B-GEP', 'score': np.float32(0.99339926), 'index': 1, 'word': '▁Eesti', 'start': 0, 'end': 5}, {'entity': 'B-GEP', 'score': np.float32(0.9923631), 'index': 4, 'word': '▁Lätist', 'start': 22, 'end': 29}, {'entity': 'B-GEP', 'score': np.float32(0.990756), 'index': 6, 'word': '▁Leedust', 'start': 32, 'end': 40}, {'entity': 'B-LOC', 'score': np.float32(0.61792), 'index': 8, 'wor…7d76965 -
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