---
license: mit
language:
- en
- bn
metrics:
- f1
- accuracy
base_model:
- google-bert/bert-base-multilingual-cased
---
# EN-BN Translation Error Detection Model

This model detects translation errors in English-Bangla translations.

## Model Architecture
- Base: BERT multilingual
- Fine-tuned for multi-label classification of translation errors
- Labels: Semantic Error, Cultural Error, Literal Translation Error, Syntactical Error, No Error

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

tokenizer = AutoTokenizer.from_pretrained("SamiaHaque/ENBNErrorDetector")
model = AutoModelForSequenceClassification.from_pretrained("SamiaHaque/ENBNErrorDetector")

# Prepare input
text = "Your source and translation text here..."
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.sigmoid(outputs.logits)
```