readme: ---
  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](https://aclanthology.org/P17-1178.pdf).
  # 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
  