Instructions to use Sp1786/mutliclass-sentiment-analysis-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sp1786/mutliclass-sentiment-analysis-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sp1786/mutliclass-sentiment-analysis-bert")# Load model directly from transformers import AutoTokenizer, SentimentClassifier tokenizer = AutoTokenizer.from_pretrained("Sp1786/mutliclass-sentiment-analysis-bert") model = SentimentClassifier.from_pretrained("Sp1786/mutliclass-sentiment-analysis-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79b2fc520cec6e6c46d47c0ac5674100e8b3a36f110c37ae90ec67c4c68d46d2
- Size of remote file:
- 438 MB
- SHA256:
- 5e71e51bc72f8a45e3a35e4e40f0e549084e4f612d610ddf60e7fd518beab7ac
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