readme: 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)
  ```
  
  note that this model is trained on persian corpus and is meant to be used on persian texts too.