Instructions to use VIOLET21/sentiment-bert-tweet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VIOLET21/sentiment-bert-tweet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VIOLET21/sentiment-bert-tweet")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VIOLET21/sentiment-bert-tweet") model = AutoModelForSequenceClassification.from_pretrained("VIOLET21/sentiment-bert-tweet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Sentiment BERT Tweet
A BERT model fine-tuned for Indonesian tweet sentiment classification.
This model classifies tweets into three sentiment categories:
- Positive
- Negative
- Neutral
How to Use
from transformers import pipeline
model = pipeline("text-classification", model="VIOLET21/sentiment-bert-tweet")
result = model("Saya sangat senang hari ini!")
print(result)
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Model tree for VIOLET21/sentiment-bert-tweet
Base model
indobenchmark/indobert-base-p1