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
Commit History
-
Update README.md
aa4d28d
-
Update README.md
5cf26b0 -
Create temp_predictor.py
c316f5e -
Upload tokenizer_config.json
f159c9b -
Upload special_tokens_map.json
d7e4d86 -
Upload config.json
b0bb7e5 -
Upload scheduler.pt with git-lfs
6190a40 -
Upload merges.txt
33c3677 -
Upload training_args.bin with git-lfs
97f738a -
Upload vocab.json
a1dda9b -
Upload trainer_state.json
a9dcc5a -
Upload optimizer.pt with git-lfs
fcba6e7 -
Upload pytorch_model.bin with git-lfs
b282174 -
Update README.md
3e2cd1c -
initial commit
7dd4125