Instructions to use andyP/ro-sentiment-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andyP/ro-sentiment-01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="andyP/ro-sentiment-01")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("andyP/ro-sentiment-01") model = AutoModelForSequenceClassification.from_pretrained("andyP/ro-sentiment-01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from andyP/ro-sentiment-01: direct link, hf CLI and curl.
- Browser
- Download file 921 MB
-
https://huggingface.co/andyP/ro-sentiment-01/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://andyP/ro-sentiment-01@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/andyP/ro-sentiment-01/resolve/refs%2Fpr%2F1/optimizer.pt
921 MB
- Xet hash:
- e92663910315812482a80de8bfe19b38346f746fbb36e1115e3440172a976935
- Size of remote file:
- 921 MB
- SHA256:
- 69a808027505eb7fa7696ef3a77a2667eb18f6f8a73cec823b3dd51fe9688f78
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.