SentimentBERT is a Finetuned BERT-based model specifically for sentiment classification of sentences into three categories: Positive, Negative, and Neutral.

This model has been trained on a ** 130K large and diverse dataset of news articles** across a wide range of categories. It achieves over 86% accuracy and demonstrates a strong understanding of sentence-level sentiment, even in nuanced or mixed-context cases.

Model Highlights

Base model: bert-base-uncased
Fine tuned for: Sentiment classification (3-class)
Accuracy: > 86%
Classes: Positive, Neutral, Negative
Language: English
Format: safetensors
Tokenizer: Compatible with bert-base-uncased

Applications

This model is well-suited for:

News article sentiment analysis
Amazon product review analysis
Customer support or service feedback systems
General-purpose opinion mining

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How to use the model

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained("mervp/SentimentBERT")
tokenizer = AutoTokenizer.from_pretrained("mervp/SentimentBERT")

def predict_sentiment(text):
    model.eval()
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        prediction = torch.argmax(logits, dim=-1).item()
    label = model.config.id2label[prediction]
    return label

print(predict_sentiment("What a beautiful day."))               # positive
print(predict_sentiment("The service was excellent."))          # positive
print(predict_sentiment("He did a fantastic job."))             # positive
print(predict_sentiment("The experience was terrible."))        # negative
print(predict_sentiment("Everything went wrong."))              # negative
print(predict_sentiment("He opened the door and walked in."))   # neutral
print(predict_sentiment("They are meeting at 5 PM."))           # neutral
print(predict_sentiment("She has a cat."))                      # neutral