Instructions to use sibstrider/rubert-tiny2-cuda-finetuned-frozen-3-classification_ffffff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sibstrider/rubert-tiny2-cuda-finetuned-frozen-3-classification_ffffff with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sibstrider/rubert-tiny2-cuda-finetuned-frozen-3-classification_ffffff")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sibstrider/rubert-tiny2-cuda-finetuned-frozen-3-classification_ffffff") model = AutoModelForSequenceClassification.from_pretrained("sibstrider/rubert-tiny2-cuda-finetuned-frozen-3-classification_ffffff", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sibstrider/rubert-tiny2-cuda-finetuned-frozen-3-classification_ffffff: direct link, hf CLI and curl.
- Browser
- Download file 117 MB
-
https://huggingface.co/sibstrider/rubert-tiny2-cuda-finetuned-frozen-3-classification_ffffff/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sibstrider/rubert-tiny2-cuda-finetuned-frozen-3-classification_ffffff/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sibstrider/rubert-tiny2-cuda-finetuned-frozen-3-classification_ffffff/resolve/main/pytorch_model.bin
117 MB
- Xet hash:
- a68ddc58d904740de0493f10e4f0fdaaf92f158285de73c88b74502cdf95f8d3
- Size of remote file:
- 117 MB
- SHA256:
- 2cffcdd0bee0ef1d5ba97b31c156b3689a07a6b8a6c4da10afda7299910e57f6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.