Token Classification
Transformers
Safetensors
Azerbaijani
xlm-roberta
ner
multilingual
azerbaijani
Eval Results (legacy)
Instructions to use ismatsamadov/xlm-roberta-az-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ismatsamadov/xlm-roberta-az-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ismatsamadov/xlm-roberta-az-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ismatsamadov/xlm-roberta-az-ner") model = AutoModelForTokenClassification.from_pretrained("ismatsamadov/xlm-roberta-az-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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# XLM-RoBERTa Azerbaijani NER Model
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[](https://huggingface.co/IsmatS/xlm-roberta-az-ner)
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---
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language:
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- az
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tags:
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- token-classification
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- ner
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- roberta
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- multilingual
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license: mit
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datasets:
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- LocalDoc/azerbaijani-ner-dataset
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metrics:
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- precision
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- recall
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- f1
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model-index:
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- name: XLM-RoBERTa Azerbaijani NER Model
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results:
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- task:
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name: Named Entity Recognition
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type: token-classification
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dataset:
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name: Azerbaijani NER Dataset
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type: LocalDoc/azerbaijani-ner-dataset
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metrics:
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- name: Precision
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type: precision
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value: 0.764390
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- name: Recall
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type: recall
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value: 0.740460
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- name: F1
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type: f1
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value: 0.752235
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---
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# XLM-RoBERTa Azerbaijani NER Model
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[](https://huggingface.co/IsmatS/xlm-roberta-az-ner)
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