Bert_sentiment_classifier

Bert_sentiment_classifier

A BERT (bert-base-uncased) model fine-tuned for 3-class sentiment classification:

• Positive
• Neutral
• Negative

Labels

Test Drive

Try one of these examples into the widget:

• Negative: "Dirty. Generally poor attitude among the nurses, even the good know the place sucks. When patients are crying for help nurse should not be busy watching Tik-Tok. Too many mistakes made too often. Teaching nurses instructing student nurse procedures incorrectly. Yes, it is bad."
• Neutral: "I received the update and will review it later this week."
• Positive: "I had surgery last month. and I was very impressed with the quality of service from the moment I got in till I left. Also I like to mention the nurses they were out standing"

Try it out

<div style="position:relative; width:100%; height:0; padding-bottom:75%;"> <iframe src = 'https://huggingface.co/spaces/POKWIR/bert-sentiment-demo' ></iframe> </div>

How to use

Transformers pipeline

from transformers import pipeline

clf = pipeline(
    "text-classification",
    model="pokwir/Bert_sentiment_classifier",
    tokenizer="pokwir/Bert_sentiment_classifier",
    return_all_scores=True
)

texts = [
    "Dirty. Generally poor attitude among the nurses, even the good know the place sucks. When patients are crying for help nurse should not be busy watching Tik-Tok. Too many mistakes made too often. Teaching nurses instructing student nurse procedures incorrectly. Yes, it is bad.",
    "I had surgery last month. and I was very impressed with the quality of service from the moment I got in till I left. Also I like to mention the nurses they were out standing.",
    "This hospital has been going downhill for years thanks to dr.billie and her know all attitude she should go back to her vet clinic."
]

print(clf(texts))