metadata
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
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