Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 1368152708
• CO2 Emissions (in grams): 0.0603

Usage

Python API:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F

tokenizer = AutoTokenizer.from_pretrained("SalmanFaroz/dark_IntentCLF")
model = AutoModelForSequenceClassification.from_pretrained("SalmanFaroz/dark_IntentCLF")

# Define your input sequence
input_text = "I love AutoTrain"

# Tokenize your input sequence
inputs = tokenizer(input_text, return_tensors="pt")

# Pass the inputs to the model's forward method to get the logits
outputs = model(**inputs)
logits = outputs.logits

# Apply a softmax function to the logits to get the output probabilities
probs = F.softmax(logits, dim=1)

# Convert the tensor of output probabilities to a dictionary
class_probs = {model.config.id2label[i]: prob.item() for i, prob in enumerate(probs[0])}

print(class_probs)