Text Classification
Transformers
Safetensors
Indonesian
English
xlm-roberta
sentiment-analysis
indonesian
multilingual
social-media
text-embeddings-inference
Instructions to use nahiar/sentiment-analysis-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nahiar/sentiment-analysis-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nahiar/sentiment-analysis-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nahiar/sentiment-analysis-v2") model = AutoModelForSequenceClassification.from_pretrained("nahiar/sentiment-analysis-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 589497b1128da16fb0dc8e7ff1ebec6889484c72bb70c49c5ba248ce0d9a2897
- Size of remote file:
- 14.6 kB
- SHA256:
- f806cb7ce9eb8a3b06b4e9b29c4fedbba14df0bec195e5ab719d01674f52f80f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.