Instructions to use joyebright/EAMT2023-Baseline-RU-EN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joyebright/EAMT2023-Baseline-RU-EN with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="joyebright/EAMT2023-Baseline-RU-EN")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("joyebright/EAMT2023-Baseline-RU-EN") model = AutoModelForSequenceClassification.from_pretrained("joyebright/EAMT2023-Baseline-RU-EN", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from joyebright/EAMT2023-Baseline-RU-EN: direct link, hf CLI and curl.
- Browser
- Download file 4.35 MB
-
https://huggingface.co/joyebright/EAMT2023-Baseline-RU-EN/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://joyebright/EAMT2023-Baseline-RU-EN@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/joyebright/EAMT2023-Baseline-RU-EN/resolve/refs%2Fpr%2F1/optimizer.pt
4.35 MB
- Xet hash:
- 2b704c9eaf7a78a70fc893030fae64ca78c8bfc42e018674501a837a16cc70f8
- Size of remote file:
- 4.35 MB
- SHA256:
- 53091f41fce233d25966d29369a0509d53808016e411f175a3eb8b9d6430d795
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.