added tensorflow usage

README.md

@@ -9,7 +9,7 @@ The dataset downloaded from interpress. This dataset is real world data. Actuall
 ## Model
 Model accuracy on train data and validation data is %97.
 
-## Usage
+## Usage for Torch
 ```sh
 pip install transformers or pip install transformers==4.3.3
 ```
@@ -17,7 +17,6 @@ pip install transformers or pip install transformers==4.3.3
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 tokenizer = AutoTokenizer.from_pretrained("serdarakyol/interpress-turkish-news-classification")
-
 model = AutoModelForSequenceClassification.from_pretrained("serdarakyol/interpress-turkish-news-classification")
 ```
 
@@ -44,7 +43,7 @@ def prediction(news):
     return_attention_mask=True,
     padding='max_length',
     truncation=True,
-    return_tensors='pt') # for tf tensors, switch pt to tf
+    return_tensors='pt')
 
     inputs = indices["input_ids"].clone().detach().to(device)
     masks = indices["attention_mask"].clone().detach().to(device)
@@ -78,7 +77,28 @@ pred = prediction(news)
 print(labels[pred])
 # > World
 ```
-Thanks to @yavuzkomecoglu for contributes
+## Usage for Tensorflow
+```sh
+pip install transformers or pip install transformers==4.3.3
+
+import tensorflow as tf
+from transformers import BertTokenizer, TFBertForSequenceClassification
+import numpy as np
+
+tokenizer = BertTokenizer.from_pretrained('serdarakyol/interpress-turkish-news-classification')
+model = TFBertForSequenceClassification.from_pretrained("serdarakyol/interpress-turkish-news-classification")
+
+inputs = tokenizer(news, return_tensors="tf")
+inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1
+
+outputs = model(inputs)
+loss = outputs.loss
+logits = outputs.logits
+pred = np.argmax(logits,axis=1)[0]
+labels[pred]
+# > World
+```
+Thanks to [@yavuzkomecoglu](https://huggingface.co/yavuzkomecoglu) for contributes
 
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