Update README.md

README.md

@@ -31,7 +31,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # mT5-base-turkish-qa
 
-This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
+This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the [ucsahin/TR-Extractive-QA-82K](https://huggingface.co/datasets/ucsahin/TR-Extractive-QA-82K) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.5109
 - Rouge1: 79.3283
@@ -48,21 +48,41 @@ mT5-base model is trained with manually curated Turkish dataset consisting of 65
 The intended use of the model is extractive question answering.
 
 In order to use the inference widget, enter your input in the format:
-"""
+```
 Soru: question_text
 Metin: context_text
-"""
+```
 
 Generated response by the model:
-"""
+```
 Cevap: answer_text
-"""
+```
 
-## Training and evaluation data
+Use with Transformers:
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from datasets import load_dataset
 
-More information needed
+# Load the dataset
+qa_tr_datasets = load_dataset("ucsahin/TR-Extractive-QA-82K")
 
-## Training procedure
+# Load model and tokenizer
+model_checkpoint = "ucsahin/mT5-base-turkish-qa"
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+
+inference_dataset = qa_tr_datasets["test"].select(range(10))
+
+for input in inference_dataset:
+    input_question = "Soru: " + input["question"]
+    input_context = "Metin: " + input["context"]
+
+    tokenized_inputs = tokenizer(input_question, input_context, max_length=512, truncation=True, return_tensors="pt")
+    outputs = model.generate(input_ids=tokenized_inputs["input_ids"], max_new_tokens=32)
+    output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+    print(f"Reference answer: {input['answer']}, Model Answer: {output_text}")
+```
 
 ### Training hyperparameters