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@@ -39,9 +39,14 @@ This model is well-suited for:
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  - **Customer support or service feedback systems**
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  - **General-purpose opinion mining**
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  ---
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- ## Usage Example
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  ```python
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
@@ -68,10 +73,3 @@ print(predict_sentiment("Everything went wrong.")) # negative
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  print(predict_sentiment("He opened the door and walked in.")) # neutral
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  print(predict_sentiment("They are meeting at 5 PM.")) # neutral
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  print(predict_sentiment("She has a cat.")) # neutral
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- Thanks for visiting and downloading this model!
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- If this model helped you, please consider leaving a 👍 like. Your support helps this model reach more developers and encourages further improvements if any.
 
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  - **Customer support or service feedback systems**
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  - **General-purpose opinion mining**
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+
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+
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+ Thanks for visiting and downloading this model!
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+ If this model helped you, please consider leaving a 👍 like. Your support helps this model reach more developers and encourages further improvements if any.
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+
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  ---
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+ ## How to use the model
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  ```python
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
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  print(predict_sentiment("He opened the door and walked in.")) # neutral
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  print(predict_sentiment("They are meeting at 5 PM.")) # neutral
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  print(predict_sentiment("She has a cat.")) # neutral