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xlm-roberta-base-turkish-ner
akdeniz27/xlm-roberta-base-turkish-ner
xlm-roberta-base-turkish-ner is a token classification model from akdeniz27. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
This model is the fine-tuned version of "xlm-roberta-base" (a multilingual version of RoBERTa) using a reviewed version of well known Turkish NER dataset (https://github.com/stefan-it/turkish-bert/files/4558187/nerdat…
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Model card
From the Hugging Face model README
Turkish Named Entity Recognition (NER) Model
This model is the fine-tuned version of "xlm-roberta-base" (a multilingual version of RoBERTa) using a reviewed version of well known Turkish NER dataset (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt).
Fine-tuning parameters:
task = "ner"
model_checkpoint = "xlm-roberta-base"
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 = 2
weight_decay = 0.01
How to use:
model = AutoModelForTokenClassification.from_pretrained("akdeniz27/xlm-roberta-base-turkish-ner")
tokenizer = AutoTokenizer.from_pretrained("akdeniz27/xlm-roberta-base-turkish-ner")
ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
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: 0.9919343118732742
- f1: 0.9492100796448622
- precision: 0.9407349896480332
- recall: 0.9578392621870883
- Task
- Token Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Languages
- tr
- Created
- Mar 2, 2022
- Updated
- Jun 18, 2024