Instructions to use candra/base-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use candra/base-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="candra/base-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("candra/base-sentiment") model = AutoModelForSequenceClassification.from_pretrained("candra/base-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0138550f80db7eaf422e18a0527c65e7eb9a14a4fbdeaecb096e7eab8b3d5493
- Size of remote file:
- 876 MB
- SHA256:
- 4316ce99619bca8917c6bdfe56e0f1b7026c956fd0e42c9a5d5ffa659ba07150
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