Update README.md

Update model card

README.md

@@ -5,16 +5,53 @@ language:
 tags:
 - mt5
 - t5
+- text-generation-inference
+- turkish
 widget:
-- text: "Bu hafta hasta olduğum için <extra_id_0> gittim. Midem ağrıyordu ondan dolayı şu an <extra_id_1>."
-- example_title: "Turkish Example 1"
-- text: "Bu gece kar yağacakmış. Yarın yollarda <extra_id_0> olabilir."
-- example_title: "Turkish Example 2"
-- text: "I bought two tickets for NBA match. Do you like <extra_id_0> ?"
-- example_title: "English Example 2"
+- text: >-
+    Bu hafta hasta olduğum için <extra_id_0> gittim. Midem ağrıyordu ondan
+    dolayı şu an <extra_id_1>.
+- example_title: Turkish Example 1
+- text: Bu gece kar yağacakmış. Yarın yollarda <extra_id_0> olabilir.
+- example_title: Turkish Example 2
+- text: I bought two tickets for NBA match. Do you like <extra_id_0> ?
+- example_title: English Example 2
 ---
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
-Please check **google/mt5-base** model. This model is pruned version of mt5-base model to only work in Turkish and English.
+Please check [**google/mt5-base**](https://huggingface.co/google/mt5-base) model. This model is pruned version of mt5-base model to only work in Turkish and English. Also for methodology, you can check Russian version of mT5-base [cointegrated/rut5-base](https://huggingface.co/cointegrated/rut5-base).
 
+# Usage
+
+You should import required libraries by:
+```python
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import torch
+```
+
+To load model:
+```python
+model = T5ForConditionalGeneration.from_pretrained('bonur/t5-base-tr')
+tokenizer = T5Tokenizer.from_pretrained('bonur/t5-base-tr')
+```
+
+To make inference with given text, you can use the following code:
+
+
+To get the sentence embeddings, you can  use the following code:
+```python
+inputs = tokenizer("Bu hafta hasta olduğum için <extra_id_0> gittim.", return_tensors='pt')
+with torch.no_grad():
+    hypotheses = model.generate(
+        **inputs,
+        do_sample=True, top_p=0.95,
+        num_return_sequences=2,
+        repetition_penalty=2.75,
+        max_length=32,
+    )
+for h in hypotheses:
+    print(tokenizer1.decode(h))
+```
+
+You can tune parameters for better result, and this model is ready to fine-tune in downstream tasks which utilizes bilingual with English and Turkish.