Instructions to use tr3cks/SentimentAnalysis_BETO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tr3cks/SentimentAnalysis_BETO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tr3cks/SentimentAnalysis_BETO")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tr3cks/SentimentAnalysis_BETO") model = AutoModelForSequenceClassification.from_pretrained("tr3cks/SentimentAnalysis_BETO") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a00c55da78038c0470964b5cd0bb015ac5cfcb8d1a0788aac01954062a760bc
- Size of remote file:
- 439 MB
- SHA256:
- 63d3102494716d7c82bea96f0ac96cca999c2ebdd4d99d5a04d3decb7d35f797
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