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