# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("RvKy/bert_FineTuned_MultiClass_news")
model = AutoModelForSequenceClassification.from_pretrained("RvKy/bert_FineTuned_MultiClass_news")Quick Links
We use the trt turkish news data which inside news context and categories belongs to one of the news.
Using bert base turkish uncased model, aimed to label the categories to the news.
We have 11 separate categories as below;
('bilim_teknoloji',
'dunya', 'egitim',
'ekonomi',
'guncel',
'gundem',
'kultur_sanat',
'saglik',
'spor',
'turkiye',
'yasam')
We got the validation skor and follow the metric accuracy. The model gave us successfully result.
Training results
| Epoch | Train Loss | Validation Loss | accuracy | val_accuracy |
|---|---|---|---|---|
| 0 | 0.739859 | 0.507217 | 0.766797 | 0.828693 |
| 1 | 0.413323 | 0.474160 | 0.865625 | 0.843466 |
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Model tree for RvKy/bert_FineTuned_MultiClass_news
Base model
dbmdz/bert-base-turkish-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RvKy/bert_FineTuned_MultiClass_news")