Instructions to use Rustem/roberta-base-trained-7epochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rustem/roberta-base-trained-7epochs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Rustem/roberta-base-trained-7epochs")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Rustem/roberta-base-trained-7epochs") model = AutoModelForMaskedLM.from_pretrained("Rustem/roberta-base-trained-7epochs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from Rustem/roberta-base-trained-7epochs: direct link, hf CLI and curl.
- Browser
- Download file 998 MB
-
https://huggingface.co/Rustem/roberta-base-trained-7epochs/resolve/main/optimizer.pt
- Command line
-
hf download hf://Rustem/roberta-base-trained-7epochs/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Rustem/roberta-base-trained-7epochs/resolve/main/optimizer.pt
998 MB
- Xet hash:
- 933daa8b3cabb9160f883ff016df47497383905bb97f0756c71d853f54daf964
- Size of remote file:
- 998 MB
- SHA256:
- fe5f972100df9e6144583796d61d817b1a73b062dab5932eeb008f59bd3f2a47
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.