Instructions to use Agra2002/sentiment_analysis_LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Agra2002/sentiment_analysis_LLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Agra2002/sentiment_analysis_LLM")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Agra2002/sentiment_analysis_LLM") model = AutoModelForSequenceClassification.from_pretrained("Agra2002/sentiment_analysis_LLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f7c78a4d16a266b94879e555d62ec1e794d111d63c41a5de704dd9777379eca
- Size of remote file:
- 499 MB
- SHA256:
- 3aead81fbdb4611873df5ec497ac2f853236d2743e3192b6c302440625bdd87c
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