Instructions to use nizamovtimur/rubert-tiny-reports-covid-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nizamovtimur/rubert-tiny-reports-covid-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nizamovtimur/rubert-tiny-reports-covid-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nizamovtimur/rubert-tiny-reports-covid-classification") model = AutoModelForSequenceClassification.from_pretrained("nizamovtimur/rubert-tiny-reports-covid-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nizamovtimur/rubert-tiny-reports-covid-classification: direct link, hf CLI and curl.
- Browser
- Download file 117 MB
-
https://huggingface.co/nizamovtimur/rubert-tiny-reports-covid-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nizamovtimur/rubert-tiny-reports-covid-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nizamovtimur/rubert-tiny-reports-covid-classification/resolve/main/pytorch_model.bin
117 MB
- Xet hash:
- 3ef631a2f53a7d8091c13fd2268cc87f9266da6d22813fef141a51659f0a9b59
- Size of remote file:
- 117 MB
- SHA256:
- 4464fa10db3dcef8d1747c5a1508013a43a1ed15cee5b7ec496fa9ea6ed5fe85
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.