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")# 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
- Xet hash:
- 723d5824823c8d37c6c9f9b8c47e7275c9f3062d632ac86cb373400fd383c1d1
- Size of remote file:
- 623 Bytes
- SHA256:
- 72064fcd70f826a657d60e17bd28db3a443ba40f942f809728164e321a607fee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.