Instructions to use dejanai/sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dejanai/sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dejanai/sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dejanai/sentiment") model = AutoModelForSequenceClassification.from_pretrained("dejanai/sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from dejanai/sentiment: direct link, hf CLI and curl.
- Browser
- Download file 93.5 MB
-
https://huggingface.co/dejanai/sentiment/resolve/main/optimizer.pt
- Command line
-
hf download hf://dejanai/sentiment/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dejanai/sentiment/resolve/main/optimizer.pt
93.5 MB
- Xet hash:
- 5d1f7c23b3e62aea5a83e2bf7d9415c08ef7a9c513088cfb765130f033357486
- Size of remote file:
- 93.5 MB
- SHA256:
- 704f791ee5f506d1446755aba52655f550919e98e43e56ccdc85c0193627fb08
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.