# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("interneuronai/customer_feedback_analysis_-_company_x_bart")
model = AutoModelForSequenceClassification.from_pretrained("interneuronai/customer_feedback_analysis_-_company_x_bart", device_map="auto")Quick Links
Customer Feedback Analysis - Company X
Description: Classify customer feedback based on sentiment, topic, and urgency. Prioritize and address customer concerns, improve products and services, and enhance customer satisfaction.
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/customer_feedback_analysis_-_company_x_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))
- Downloads last month
- 3
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="interneuronai/customer_feedback_analysis_-_company_x_bart")