Instructions to use saribasmetehan/BERT-BankingClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saribasmetehan/BERT-BankingClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="saribasmetehan/BERT-BankingClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("saribasmetehan/BERT-BankingClassifier") model = AutoModelForSequenceClassification.from_pretrained("saribasmetehan/BERT-BankingClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Model description
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This model is a BERT (bert-base-uncased) model fine-tuned using the Banking77 dataset for text classification tasks. Banking77 is a dataset designed to categorize customer support messages in the banking and finance domain into 77 distinct categories. This model is specifically tailored for classifying customer complaints and inquiries in the banking sector, helping to assign messages to the correct categories effectively.
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| Label Index | Intent Category |
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## Model description
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This model is a BERT (bert-base-uncased) model fine-tuned using the Banking77 dataset for text classification tasks. Banking77 is a dataset designed to categorize customer support messages in the banking and finance domain into 77 distinct categories. This model is specifically tailored for classifying customer complaints and inquiries in the banking sector, helping to assign messages to the correct categories effectively.
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Fine-Tuning Process : https://github.com/saribasmetehan/bank_administrative_assistant/blob/main/BERT_BankingClassifier.ipynb
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| Label Index | Intent Category |
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