metadata

language: sq
widget:
  - text: >-
      Varianti AY.4.2 është më i lehtë për t'u transmetuar, thotë Francois Balu,
      drejtor i Institutit të Gjenetikës në Londër.

Albanian Named Entity Recognition (NER) Model

This model is the fine-tuned model of "bert-base-multilingual-cased" 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 = "bert-base-multilingual-cased"
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/mbert-base-albanian-cased-ner")
tokenizer = AutoTokenizer.from_pretrained("akdeniz27/mbert-base-albanian-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: 0.9719268816143276
f1: 0.9192366826444787
precision: 0.9171629669734704
recall: 0.9213197969543148