Instructions to use shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the "Fine-tune LLM.pptx" file in the GitHub repo for the theory behind this code.
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## References
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- [shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification](https://huggingface.co/shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification) by Shukdev Datta. Hugging Face. Accessed on 02-09-2024.
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Check out the "Fine-tune LLM.pptx" file in the GitHub repo for the theory behind this code.
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