Instructions to use CogComp/roberta-temporal-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CogComp/roberta-temporal-predictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CogComp/roberta-temporal-predictor")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CogComp/roberta-temporal-predictor") model = AutoModelForMaskedLM.from_pretrained("CogComp/roberta-temporal-predictor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from CogComp/roberta-temporal-predictor: direct link, hf CLI and curl.
- Browser
- Download file 998 MB
-
https://huggingface.co/CogComp/roberta-temporal-predictor/resolve/main/optimizer.pt
- Command line
-
hf download hf://CogComp/roberta-temporal-predictor/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/CogComp/roberta-temporal-predictor/resolve/main/optimizer.pt
998 MB
- Xet hash:
- fcae89ea57d24c5deba248a875d0dbe44c3d5afa66d453fb5a42eeccaa6efb52
- Size of remote file:
- 998 MB
- SHA256:
- 15214fdfe7f7fbffe78e3427df31d2ff96f54eaab4829342d856f3873fc1ca8d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.