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README.md

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+ParsBERT digikala sentiment analysis model fine-tuned on around 600,000 Persian tweets.
+# How to use
+at least you need 650 megabytes of ram and disk in order to load the model.
+tensorflow, transformers and numpy library
+
+## Loading model
+```python
+import numpy as np
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+#loading model
+tokenizer = AutoTokenizer.from_pretrained("nimaafshar/parsbert-fa-sentiment-twitter")
+  
+model = TFAutoModelForSequenceClassification.from_pretrained("nimaafshar/parsbert-fa-sentiment-twitter")
+classes = ["negative","neutral","positive"]
+```
+## Using Model
+```python
+
+#using model
+sequences = [".غذا خیلی افتضاح بود متاسفم برای مدیریت رستورن خیلی بد بود.",
+ "خیلی خوشمزده و عالی بود عالی",
+"می‌تونم اسمتونو بپرسم؟"
+]
+
+for sequence in sequences:
+  inputs = tokenizer(sequence, return_tensors="tf")
+  classification_logits = model(inputs)[0]
+  results = tf.nn.softmax(classification_logits, axis=1).numpy()[0]
+  print(classes[np.argmax(results)])
+  percentages = np.around(results*100)
+  print(percentages)
+```