Instructions to use niltonseixas/sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niltonseixas/sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="niltonseixas/sentiment_analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("niltonseixas/sentiment_analysis") model = AutoModelForSequenceClassification.from_pretrained("niltonseixas/sentiment_analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from niltonseixas/sentiment_analysis: direct link, hf CLI and curl.
- Browser
- Download file 263 MB
-
https://huggingface.co/niltonseixas/sentiment_analysis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://niltonseixas/sentiment_analysis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/niltonseixas/sentiment_analysis/resolve/main/pytorch_model.bin
263 MB
- Xet hash:
- e0a5c7eb2df1845dbc4054c1890106f8a9c1d6a638d6b9ba6fd0c54924bd5cd4
- Size of remote file:
- 263 MB
- SHA256:
- 69cfb59355006e44335ac632fa3fbeeacf4dcf970f0d72a689f128c1a0b868a5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.