Instructions to use sbcBI/sentiment_analysis_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sbcBI/sentiment_analysis_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sbcBI/sentiment_analysis_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sbcBI/sentiment_analysis_model") model = AutoModelForSequenceClassification.from_pretrained("sbcBI/sentiment_analysis_model", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sbcBI/sentiment_analysis_model: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/sbcBI/sentiment_analysis_model/resolve/main/training_args.bin
- Command line
-
hf download hf://sbcBI/sentiment_analysis_model/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/sbcBI/sentiment_analysis_model/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- 608ed7eee6cc937d33e15c5b16883defa9793e673736dffe213235b184dbbe55
- Size of remote file:
- 2.99 kB
- SHA256:
- 040ed6973ce0048751190a0f0933302ddc65861ed3a37dca9fce1fefe424f0c2
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