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---
language:
- "tr"
- tr
thumbnail: "url to a thumbnail used in social sharing"
tags:
- tag1
- tag2
license: "bigscience-openrail-m"
datasets:
- https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset
---
# Turkish Question Answering model based on mt0-large
In this model, I fine-tuned *mT0-large* model with the following Turkish QA datasets where the training set size is around 11K QAs.
* https://huggingface.co/bigscience/mt0-large
* https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset
The model is tuned within one of parameter-efficient fine-tuning techniques, which is PEFT LORA. So we need to install peft modules. Please check
* https://github.com/huggingface/peft
# FOR INFERENCE
We first install the requirements :
```
!pip install peft transformers # in jupyter notebook
```
## SINGLE INFERENCE
```
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
peft_model_path="savasy/mt0-large-Turkish-qa"
config = PeftConfig.from_pretrained(peft_model_path)
model = AutoModelForSeq2SeqLM.from_pretrained(
config.base_model_name_or_path)
#tokenizer
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Wrap the model with the LORA
inference_model = PeftModel.from_pretrained(model, peft_model_path)
inference_model.eval()
inference_model.to("cuda")
test_input = '''
Mustafa adını babası Ali Rıza Efendi kendi dedesinin adı olduğundan dolayı vermiştir. Çünkü Ali Rıza Efendi'nin babasının adı olan
Ahmed adı ağabeylerinden birisine verilmişti. Mustafa'ya neden Kemal isminin verildiğine yönelik ise çeşitli iddialar vardır.
Afet İnan, bu ismi ona matematik öğretmeni Üsküplü Mustafa Efendi'nin Kemal adının anlamında olduğu gibi onun "mükemmel ve olgun"
olduğunu göstermek için verdiğini söylemiştir. (source: wikipedia) .
Mustafa'nın dedesinin ismi nedir ?
'''
import torch
with torch.no_grad():
inputs = tokenizer(test_input, return_tensors="pt", padding=True).to("cuda")
generated_ids = inference_model.generate(**inputs)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
outputs
-> [Ahmed]
```
## BATCH INFERENCE
The usage for batch mode is as follows:
```
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
peft_model_path="savasy/mt0-large-Turkish-qa"
config = PeftConfig.from_pretrained(peft_model_path)
model = AutoModelForSeq2SeqLM.from_pretrained(
config.base_model_name_or_path)
#tokenizer
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Wrap the model with the LORA
inference_model = PeftModel.from_pretrained(model, peft_model_path)
inference_model.eval()
inference_model.to("cuda")
test_inputs = ["","","",""] # a list of texts. A text must have Content followed by a Question
preds=[] # for predictions
data_loader= DataLoader(test_inputs,batch_size=8)
from tqdm import tqdm
with torch.no_grad():
for batch in tqdm(data_loader):
inputs = tokenizer(batch, return_tensors="pt", padding=True).to("cuda")
generated_ids = inference_model.generate(**inputs)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
preds+=outputs
```
In the end, you can compare *preds* (predictions) with your expected ground-truth results. You can use many metrics for this such as BLEU, ROUGE, Exact Match, Partial Match
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