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--- |
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language: tr |
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library_name: peft |
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pipeline_tag: token-classification |
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base_model: dbmdz/bert-base-turkish-cased |
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--- |
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## Training procedure |
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This is a fine-tuned model of base model "dbmdz/bert-base-turkish-cased" using the Parameter Efficient Fine Tuning (PEFT) with Low-Rank Adaptation (LoRA) technique |
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using a reviewed version of well known Turkish NER dataset |
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(https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt). |
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trainable params: 702,734 || all params: 110,627,342 || trainable%: 0.6352263258752072 |
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# Fine-tuning parameters: |
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``` |
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task = "ner" |
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model_checkpoint = "dbmdz/bert-base-turkish-cased" |
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batch_size = 16 |
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label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'] |
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max_length = 512 |
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learning_rate = 1e-3 |
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num_train_epochs = 7 |
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weight_decay = 0.01 |
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``` |
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# PEFT Parameters |
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``` |
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inference_mode=False |
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r=16 |
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lora_alpha=16 |
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lora_dropout=0.1 |
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bias="all" |
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``` |
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# How to use: |
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``` |
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peft_model_id = "akdeniz27/bert-base-turkish-cased-ner-lora" |
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config = PeftConfig.from_pretrained(peft_model_id) |
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inference_model = AutoModelForTokenClassification.from_pretrained( |
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config.base_model_name_or_path, num_labels=7, id2label=id2label, label2id=label2id |
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) |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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model = PeftModel.from_pretrained(inference_model, peft_model_id) |
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text = "Mustafa Kemal Atatürk 1919 yılında Samsun'a çıktı." |
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inputs = tokenizer(text, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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tokens = inputs.tokens() |
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predictions = torch.argmax(logits, dim=2) |
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for token, prediction in zip(tokens, predictions[0].numpy()): |
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print((token, model.config.id2label[prediction])) |
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``` |
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# Reference test results: |
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* accuracy: 0.993297 |
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* f1: 0.949696 |
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* precision: 0.942554 |
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* recall: 0.956946 |