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