msbayindir
/

sample-mt5-small

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 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
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 ---
 library_name: transformers
+language:
+- tr
+metrics:
+- bleu
+pipeline_tag: question-answering
 ---
 # Model Card for Model ID
 ## Model Details
+<p>
+This model has been trained with the dataset
+        <a href="https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset">Turkish-Reading-Comprehension-Question-Answering-Dataset </a>for Turkish question-answering
+</p>
+<h1>Usage</h1>
+  <h2>install package</h2>
+  <ul>
+          <li>!pip install transformers</li>
+          <li>!pip install evaluate</li>
+          <li>!pip install rouge</li>
+  </ul>
+  <h2>Codes</h2>
+  <h3>Imports</h3>
+```python
+import torch
+import nltk
+import string
+import evaluate  # Bleu
+import pandas as pd
+import numpy as np
+from transformers import T5Tokenizer, MT5Model, MT5ForConditionalGeneration, MT5TokenizerFast
+import warnings
+```
+```python
+TOKENIZER = MT5TokenizerFast.from_pretrained("msbayindir/sample-mt5-small")
+MODEL = MT5ForConditionalGeneration.from_pretrained("msbayindir/sample-mt5-small", return_dict=True)
+DEVICE = "cuda:0"
+MODEL = MODEL.to(device=DEVICE)
+Q_LEN = 256   # Question Length
+T_LEN = 32
+```
+```python
+def predict_answer(context, question, ref_answer=None):
+    inputs = TOKENIZER(question, context, max_length=Q_LEN, padding="max_length", truncation=True, add_special_tokens=True)
+    input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).to(DEVICE).unsqueeze(0)
+    attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).to(DEVICE).unsqueeze(0)
+    outputs = MODEL.generate(input_ids=input_ids, attention_mask=attention_mask)
+    predicted_answer = TOKENIZER.decode(outputs.flatten(), skip_special_tokens=True)
+    if ref_answer:
+        # Load the Bleu metric
+        bleu = evaluate.load("google_bleu")
+        score = bleu.compute(predictions=[predicted_answer],
+                            references=[ref_answer])
+        print("Context: \n", context)
+        print("\n")
+        print("Question: \n", question)
+        return {
+            "Reference Answer: ": ref_answer,
+            "Predicted Answer: ": predicted_answer,
+            "BLEU Score: ": score
+        }
+    else:
+        return predicted_answer
+```
+###Samples
+```python
+context = """Katmandu Büyükşehir Şehri (KMC),
+ uluslararası ilişkileri teşvik etmek amacıyla Uluslararası İlişkiler Sekreterliği (IRC) kurmuştur.
+ KMC'nin ilk uluslararası ilişkisi 1975 yılında Eugene, Oregon, Amerika Birleşik Devletleri ile kurulmuştur.
+ Bu etkinlik, diğer 8 şehirle resmi ilişkiler kurarak daha da geliştirilmiştir:
+Motsumoto City of Japan, Rochester, Myanmar Yangon (eski adıyla Rangoon),
+Çin Halk Cumhuriyeti'nden Xi'an, Belarus Minsk ve Kore Demokratik Cumhuriyeti'nden Pyongyang. KMC'nin sürekli çabası,
+Katmandu için daha iyi kentsel yönetim ve gelişim programları elde etmek için SAARC ülkeleri, diğer Uluslararası ajanslar ve
+dünyanın diğer birçok büyük şehirleri ile etkileşimini geliştirmektir."""
+answer = "Katmandu ilk uluslararası ilişkisini hangi yılda yarattı?"
+predict_answer(context,answer)
+'1975'
+```
+```python
+context = """Yapay zeka (YZ), modern dünyada hızla gelişen bir teknoloji alanıdır. YZ, sağlık, finans ve eğitim gibi birçok sektörde kullanılmaktadır.
+Sağlık alanında, YZ algoritmaları hastalık teşhislerinde yardımcı olabilir. Finans sektöründe, YZ, piyasa analizleri yaparak yatırım kararlarını destekler.
+Eğitimde ise, öğrenci performansını izler ve bireyselleştirilmiş öğrenme programları oluşturur.
+Ancak, YZ'nin kullanımı etik ve gizlilik konularında endişeler doğurur.
+Veri güvenliği ve algoritmik önyargılar, dikkatle ele alınması gereken önemli meselelerdir."""
+answer = "Yapay zeka hangi sektörlerde kullanılmaktadır?"
+predict_answer(context,answer)
+'sağlık, finans ve eğitim'
+```