Migrate model card from transformers-repo
Browse filesRead announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/gurkan08/bert-turkish-text-classification/README.md
README.md
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---
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language: tr
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---
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# Turkish News Text Classification
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Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)
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# Dataset
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Dataset consists of 11 classes were obtained from https://www.trthaber.com/. The model was created using the most distinctive 6 classes.
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Dataset can be accessed at https://github.com/gurkan08/datasets/tree/master/trt_11_category.
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label_dict = {
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'LABEL_0': 'ekonomi',
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'LABEL_1': 'spor',
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'LABEL_2': 'saglik',
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'LABEL_3': 'kultur_sanat',
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'LABEL_4': 'bilim_teknoloji',
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'LABEL_5': 'egitim'
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}
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70% of the data were used for training and 30% for testing.
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train f1-weighted score = %97
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test f1-weighted score = %94
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# Usage
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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("gurkan08/bert-turkish-text-classification")
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model = AutoModelForSequenceClassification.from_pretrained("gurkan08/bert-turkish-text-classification")
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nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
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text = ["Süper Lig'in 6. haftasında Sivasspor ile Çaykur Rizespor karşı karşıya geldi...",
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"Son 24 saatte 69 kişi Kovid-19 nedeniyle yaşamını yitirdi, 1573 kişi iyileşti"]
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out = nlp(text)
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label_dict = {
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'LABEL_0': 'ekonomi',
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'LABEL_1': 'spor',
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'LABEL_2': 'saglik',
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'LABEL_3': 'kultur_sanat',
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'LABEL_4': 'bilim_teknoloji',
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'LABEL_5': 'egitim'
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}
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results = []
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for result in out:
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result['label'] = label_dict[result['label']]
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results.append(result)
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print(results)
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# > [{'label': 'spor', 'score': 0.9992026090621948}, {'label': 'saglik', 'score': 0.9972177147865295}]
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