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akdeniz27/bert-base-hungarian-cased-ner

🤗 Hugging Face sourcetoken-classificationmit110M params440 MBsafetensors✓ 3 checksumsupdated today
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Hungarian Named Entity Recognition (NER) Model

This model is the fine-tuned model of "SZTAKI-HLT/hubert-base-cc" using the famous WikiANN dataset presented in the "Cross-lingual Name Tagging and Linking for 282 Languages" paper.

Fine-tuning parameters:

task = "ner"
model_checkpoint = "SZTAKI-HLT/hubert-base-cc"
batch_size = 8 
label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']
max_length = 512 
learning_rate = 2e-5 
num_train_epochs = 3 
weight_decay = 0.01

How to use:

model = AutoModelForTokenClassification.from_pretrained("akdeniz27/bert-base-hungarian-cased-ner")
tokenizer = AutoTokenizer.from_pretrained("akdeniz27/bert-base-hungarian-cased-ner")
ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first")
ner("<your text here>")

Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter.

Reference test results:

  • accuracy: 0.9774538310923768
  • f1: 0.9462099085573904
  • precision: 0.9425718667406271
  • recall: 0.9498761426661113