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# MT5-Small Fine-tuned on Arabic Question Answering
This model is a fine-tuned version of MT5-Small for question answering tasks in Arabic.
## Training and evaluation data
The model was trained on the tydiqa-goldp dataset for Arabic.
## Training procedure
The model was fine-tuned using the Hugging Face Transformers library.
## How to use
You can use this model with the Transformers pipeline for question answering:
```python
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = "HozRifai/mt5-ar-qa-v0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
def generate_answer(question, context, max_length=64):
input_text = f"question: {question} context: {context}"
inputs = tokenizer(input_text, return_tensors="pt", max_length=max_length, truncation=True, padding="max_length").to(device)
outputs = model.generate(
**inputs,
max_length=max_length,
num_beams=4,
length_penalty=2.0,
early_stopping=True
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
context = ""
question = ""
answer = generate_answer(question, context)
print("Answer is: ", answer)
``` |