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--- |
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language: |
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- en |
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base_model: |
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- google-bert/bert-base-uncased |
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pipeline_tag: text2text-generation |
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tags: |
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- user |
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- intent |
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- intention |
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- recognition |
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widget: |
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- text: "Sounds good. I'm interested in trying the free trial. How do I sign up?" |
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--- |
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## Get Started |
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### Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model = AutoModelForSequenceClassification.from_pretrained("Savtale/User-Intention-Recognition") |
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tokenizer = AutoTokenizer.from_pretrained("Savtale/User-Intention-Recognition") |
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# User is talking with a chatbot |
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input_user_text = "Sounds good. I'm interested in trying the free trial. How do I sign up?" |
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# Tokenizing text |
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inputs = tokenizer(input_user_text.lower(), return_tensors="pt") |
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# Predict |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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# Prepare result |
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probabilities = torch.softmax(logits, dim=-1) # Top Match |
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predicted_class = int(torch.argmax(probabilities)) # Class no. |
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str_predicted_class = class_dict[str(predicted_class)] # Class String |
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print(f"Predicted User Intention: {str_predicted_class}") # Predicted User Intention: Request a Demo |
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``` |
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### Classes |
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class_dict = { |
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"0": "Ask For Technical Support", |
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"1": "Ask General Question", |
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"2": "Start Conversation", |
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"3": "Express Dissatisfaction", |
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"4": "Request Product Information", |
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"5": "Inquire About Pricing", |
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"6": "Negotiate Price", |
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"7": "Request Return or Refund", |
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"8": "Provide Positive Feedback", |
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"9": "Provide Negative Feedback", |
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"10": "Seek Recommendation", |
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"11": "Request Customization", |
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"12": "Ask About Shipping and Delivery", |
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"13": "Inquire About Warranty and Support", |
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"14": "Express Interest in Upselling", |
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"15": "Express Interest in Cross-selling", |
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"16": "Request Urgent Assistance", |
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"17": "Ask About Promotions and Discounts", |
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"18": "Inquire About Loyalty Programs", |
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"19": "Request a Callback", |
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"20": "Ask About Payment Options", |
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"21": "Express Uncertainty", |
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"22": "Request Clarification", |
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"23": "Confirm Understanding", |
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"24": "End Conversation", |
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"25": "Express Gratitude", |
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"26": "Apologize", |
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"27": "Complain About Customer Service", |
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"28": "Request a Manager", |
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"29": "Ask About Company Policies", |
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"30": "Inquire About Job Opportunities", |
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"31": "Ask About Corporate Social Responsibility", |
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"32": "Express Interest in Investing", |
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"33": "Cancellation", |
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"34": "Ask About Return Policy", |
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"35": "Inquire About Sustainability Practices", |
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"36": "Request a Catalog", |
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"37": "Ask About Brand History", |
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"38": "Express Interest in Partnership", |
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"39": "Inquire About Franchise Opportunities", |
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"40": "Ask About Corporate Events", |
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"41": "Express Interest in Volunteering", |
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"42": "Request a Referral", |
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"43": "Ask About Gift Cards", |
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"44": "Inquire About Product Availability", |
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"45": "Request a Personalized Recommendation", |
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"46": "Ask About Order Status", |
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"47": "Express Interest in a Webinar or Workshop", |
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"48": "Request a Demo", |
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"49": "Ask About Social Media Channels" |
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} |
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## Authors |
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- Savta |
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