In [ ]:
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from scipy.special import softmax
Running cells with 'c:\Users\dell\AppData\Local\Microsoft\WindowsApps\python3.10.exe' requires ipykernel package.

Run the following command to install 'ipykernel' into the Python environment. 

Command: 'c:/Users/dell/AppData/Local/Microsoft/WindowsApps/python3.10.exe -m pip install ipykernel -U --user --force-reinstall'
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MODEL = f"cardiffnlp/twitter-roberta-base-sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
Running cells with 'c:\Users\dell\AppData\Local\Microsoft\WindowsApps\python3.10.exe' requires ipykernel package.

Run the following command to install 'ipykernel' into the Python environment. 

Command: 'c:/Users/dell/AppData/Local/Microsoft/WindowsApps/python3.10.exe -m pip install ipykernel -U --user --force-reinstall'
In [5]:
def sentiment(tweet):
    encoded_text = tokenizer(tweet,return_tensors='pt')
    output = model(**encoded_text)
    scores = output[0][0].detach().numpy()
    scores = softmax(scores)
    scores_dict = {
        'NEGATIVE' : scores[0],
        'NEUTRAL' : scores[1],
        'POSITIVE' : scores[2]
    }
    return scores_dict
In [7]:
tweet = "you're a sweet person😤"
sentiment(tweet)
Out[7]:
{'NEGATIVE': 0.85113734, 'NEUTRAL': 0.13698761, 'POSITIVE': 0.011875027}
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