Instructions to use JamesH/Movie_review_sentiment_analysis_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JamesH/Movie_review_sentiment_analysis_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JamesH/Movie_review_sentiment_analysis_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JamesH/Movie_review_sentiment_analysis_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from JamesH/Movie_review_sentiment_analysis_model: direct link, hf CLI and curl.
- Browser
- Download file 8.66 MB
-
https://huggingface.co/JamesH/Movie_review_sentiment_analysis_model/resolve/main/tokenizer.json
- Command line
-
hf download hf://JamesH/Movie_review_sentiment_analysis_model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/JamesH/Movie_review_sentiment_analysis_model/resolve/main/tokenizer.json
8.66 MB
- Xet hash:
- 43168121a890b94ca333b33694e1eed92121c01d5818f57de259c3678030727a
- Size of remote file:
- 8.66 MB
- SHA256:
- 6919623a14be4e6598f009c44e3096e4a0e889a15497bf1bbdf9c95570a1280f
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