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 model.safetensors from ninja/Sentiment_Analysis: direct link, hf CLI and curl.
- Browser
- Download file 434 MB
-
https://huggingface.co/ninja/Sentiment_Analysis/resolve/main/model.safetensors
- Command line
-
hf download hf://ninja/Sentiment_Analysis/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ninja/Sentiment_Analysis/resolve/main/model.safetensors
434 MB
- Xet hash:
- d0f2b7ce17902ce7df0a9453838569609ec57ecdc4b129d53d0db17e9bec88ef
- Size of remote file:
- 434 MB
- SHA256:
- 1d5a025af200146866b82c142b4fd6d5c588ac926c2d8d17107509eb20b9d45a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.