---
language: en
license: mit
tags:
  - cross-encoder
  - text-similarity
  - text-classification
---

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("TrishanuDas/CE-7_512_MSELoss")

# Load model weights
model = AutoModelForSequenceClassification.from_pretrained("TrishanuDas/CE-7_512_MSELoss")

# Prepare input
inputs = tokenizer("Query", "Document", return_tensors="pt", padding=True, truncation=True)

# Get prediction
with torch.no_grad():
    # Get logits
    outputs = model(**inputs)
    logits = outputs.logits
    
    # Apply sigmoid to get probabilities
    scores = torch.sigmoid(logits)
```

## Important Note
When loading this model, you need to manually apply the sigmoid function to the logits as shown in the example above.