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--- |
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datasets: |
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- bitext/Bitext-retail-banking-llm-chatbot-training-dataset |
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language: |
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- en |
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base_model: |
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- google-t5/t5-small |
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pipeline_tag: question-answering |
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tags: |
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- fintech |
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- retail-banking |
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- fine-tuning |
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- chatbot |
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- llm |
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license: cdla-sharing-1.0 |
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--- |
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# fintech-chatbot-t5 |
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## Model Description |
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This model was fine-tuned using a [retail banking chatbot dataset](https://huggingface.co/datasets/bitext/Bitext-retail-banking-llm-chatbot-training-dataset/tree/main). It is based on the T5-small architecture and is capable of answering common banking-related queries like account balances, transaction details, card activations, and more. |
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The model has been trained to generate responses to banking-related customer queries and is suited for use in automated customer service systems or virtual assistants. |
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## Model Details |
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- **Model Type:** T5-small |
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- **Training Dataset:** [retail banking chatbot dataset](https://huggingface.co/datasets/bitext/Bitext-retail-banking-llm-chatbot-training-dataset/tree/main) |
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- **Tasks:** Natural Language Generation (NLG) |
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- **Languages Supported:** English |
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## Training Details |
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- **Number of Epochs:** 3 |
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- **Training Loss:** 0.79 |
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- **Evaluation Loss:** 0.46 |
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- **Evaluation Metric:** Mean Squared Error |
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- **Batch Size:** 8 |
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## How to Use the Model |
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You can load and use this model with the following code: |
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```python |
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from transformers import T5Tokenizer, T5ForConditionalGeneration |
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tokenizer = T5Tokenizer.from_pretrained("cuneytkaya/fintech-chatbot-t5") |
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model = T5ForConditionalGeneration.from_pretrained("cuneytkaya/fintech-chatbot-t5") |
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input_text = "How can I activate my credit card?" |
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inputs = tokenizer.encode(input_text, return_tensors="pt") |
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outputs = model.generate(inputs) |
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print(tokenizer.decode(outputs[0])) |
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