---
language:
- tw
- en
license: apache-2.0
tags:
- quality-estimation
- translation
- twi
- e5
- classification
pipeline_tag: text-classification
---

# Twi–English Translation Quality Estimation

This model predicts whether a Twi–English translation pair is **correct (1)** or **incorrect (0)**.
It is based on `intfloat/multilingual-e5-small` and fine‑tuned on a synthetic dataset of word and sentence pairs.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "ghananlpcommunity/twi-eng-qe-e5"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

def predict(twi, english):
    text = f"query: {twi} passage: {english}"
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
    outputs = model(**inputs)
    prob = outputs.logits.softmax(dim=-1)
    return prob[0][1].item()   # probability of "correct"

print(predict("me ho ye", "I am fine"))        # expected > 0.5
print(predict("me ho ye", "The car is blue"))  # expected < 0.5
```

## Training Details
- **Base model:** intfloat/multilingual-e5-small
- **Data:** Human source parallel corpus (Twi–English) of 230217 examples
- **Test Accuracy:** 0.9052210928676918
- **Test F1:** 0.9152752970412363