Text Classification
Transformers
Safetensors
Turkish
bert
sentiment-analysis
finance
turkish
financial-nlp
finbert
financial bert
text-embeddings-inference
Instructions to use ff112/FinTurkBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ff112/FinTurkBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ff112/FinTurkBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ff112/FinTurkBERT") model = AutoModelForSequenceClassification.from_pretrained("ff112/FinTurkBERT") - Notebooks
- Google Colab
- Kaggle
Remove agreement-level subset section from README
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3. Apply task-adaptive pretraining on financial task text
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4. Fine-tune for 3-class sentiment classification
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For supervised fine-tuning, we evaluated multiple agreement-level subsets derived from Financial PhraseBank-style annotations:
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- `100%` agreement
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- `75%+` agreement
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- `66%+` agreement
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- `50%+` agreement
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The final model released here is the `66%+` version.
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## Evaluation Summary
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Validation results for the selected `75%+` configuration:
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3. Apply task-adaptive pretraining on financial task text
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4. Fine-tune for 3-class sentiment classification
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## Evaluation Summary
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Validation results for the selected `75%+` configuration:
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