Instructions to use rroell/RoBBERT-emotions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rroell/RoBBERT-emotions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rroell/RoBBERT-emotions")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rroell/RoBBERT-emotions") model = AutoModelForSequenceClassification.from_pretrained("rroell/RoBBERT-emotions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rroell/RoBBERT-emotions: direct link, hf CLI and curl.
- Browser
- Download file 467 MB
-
https://huggingface.co/rroell/RoBBERT-emotions/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rroell/RoBBERT-emotions/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rroell/RoBBERT-emotions/resolve/main/pytorch_model.bin
467 MB
- Xet hash:
- a52d49800890ef740e72602ee3d54c89241c525c4ff2f9c2597348713fa02200
- Size of remote file:
- 467 MB
- SHA256:
- ecefffe67cd188b07fddd74d1b3bc44e09dfc103fc96657dc7dad6f7ef42eb96
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.