Instructions to use interneuronai/bank_melli_iran_customer_service_chatbot_bart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use interneuronai/bank_melli_iran_customer_service_chatbot_bart with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="interneuronai/bank_melli_iran_customer_service_chatbot_bart")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("interneuronai/bank_melli_iran_customer_service_chatbot_bart") model = AutoModelForSequenceClassification.from_pretrained("interneuronai/bank_melli_iran_customer_service_chatbot_bart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Bank Melli Iran Customer Service Chatbot
Description: Classify customer inquiries into predefined categories and provide automated responses, improving customer service and reducing response time
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/bank_melli_iran_customer_service_chatbot_bart"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def classify_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model(**inputs)
predictions = outputs.logits.argmax(-1)
return predictions.item()
text = "Your text here"
print("Category:", classify_text(text))
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