# 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

{"language": "sq", "widget": [{"text": "Varianti AY.4.2 \u00ebsht\u00eb m\u00eb i leht\u00eb p\u00ebr t'u transmetuar, thot\u00eb Francois Balu, drejtor i Institutit t\u00eb Gjenetik\u00ebs n\u00eb Lond\u00ebr."}]}
transformers pytorch safetensors bert token-classification sq autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #token-classification #sq #autotrain_compatible #endpoints_compatible #region-us 

# 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:

# How to use: 

Pls refer "URL for entity grouping with aggregation_strategy parameter.
# Reference test results:
* accuracy: 0.9719268816143276
* f1: 0.9192366826444787
* precision: 0.9171629669734704
* recall: 0.9213197969543148

# 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: # How to use: 

Pls refer "URL for entity grouping with aggregation_strategy parameter. # Reference test results:
* accuracy: 0.9719268816143276
* f1: 0.9192366826444787
* precision: 0.9171629669734704
* recall: 0.9213197969543148
TAGS
#transformers #pytorch #safetensors #bert #token-classification #sq #autotrain_compatible #endpoints_compatible #region-us 
 # 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: # How to use: 

Pls refer "URL for entity grouping with aggregation_strategy parameter. # Reference test results:
* accuracy: 0.9719268816143276
* f1: 0.9192366826444787
* precision: 0.9171629669734704
* recall: 0.9213197969543148
passage: TAGS
#transformers #pytorch #safetensors #bert #token-classification #sq #autotrain_compatible #endpoints_compatible #region-us 
# 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:# How to use: 

Pls refer "URL for entity grouping with aggregation_strategy parameter.# Reference test results:
* accuracy: 0.9719268816143276
* f1: 0.9192366826444787
* precision: 0.9171629669734704
* recall: 0.9213197969543148