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---
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
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