Instructions to use Kurapika993/sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kurapika993/sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kurapika993/sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kurapika993/sentiment") model = AutoModelForSequenceClassification.from_pretrained("Kurapika993/sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 031dedae7368785ceded1c226cf6a81585394768c69688c0684c4326433e6ec2
- Size of remote file:
- 5.18 kB
- SHA256:
- 3248bf7d9c470770f444dacd37f4365eaba2f4957bd31095ce7889862f14590c
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