Text Classification
Transformers
PyTorch
TensorFlow
Safetensors
Indonesian
roberta
indonesian-roberta-base-sentiment-classifier
text-embeddings-inference
Instructions to use w11wo/indonesian-roberta-base-sentiment-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use w11wo/indonesian-roberta-base-sentiment-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="w11wo/indonesian-roberta-base-sentiment-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("w11wo/indonesian-roberta-base-sentiment-classifier") model = AutoModelForSequenceClassification.from_pretrained("w11wo/indonesian-roberta-base-sentiment-classifier", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
confused about the sentiment
#4
by dennamandela - opened
I want to ask, for the results of this sentiment classification, for positive, neutral, and negative, how many numbers are there for each?
Hi @dennamandela .
I'm assuming you're talking about the training set I used to train this sentiment analysis model, and how many samples of each class was in the dataset.
If so, I trained this model using SmSA from IndoNLU which contains 11,000 training samples: 6416 positive, 3436 negative, 1148 neutral.
Hope this helps!