Instructions to use ninja/Sentiment_Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ninja/Sentiment_Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ninja/Sentiment_Analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ninja/Sentiment_Analysis") model = AutoModelForSequenceClassification.from_pretrained("ninja/Sentiment_Analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ninja/Sentiment_Analysis: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/ninja/Sentiment_Analysis/resolve/f4fdeed5dd403042e3d760b141ce18dc1e0d4b8a/training_args.bin
- Command line
-
hf download hf://ninja/Sentiment_Analysis@f4fdeed5dd403042e3d760b141ce18dc1e0d4b8a/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ninja/Sentiment_Analysis/resolve/f4fdeed5dd403042e3d760b141ce18dc1e0d4b8a/training_args.bin
5.11 kB
- Xet hash:
- 7d5039928e894492771d27917c0ae5733b8d112e0a3b0cde4b4b87c191b57c8d
- Size of remote file:
- 5.11 kB
- SHA256:
- 1c258251670f7bfb2b98db4c04eb48200c676af181a61069662f8da67a7df9be
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