Instructions to use research-backup/roberta-large-semeval2012-average-prompt-d-loob with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use research-backup/roberta-large-semeval2012-average-prompt-d-loob with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="research-backup/roberta-large-semeval2012-average-prompt-d-loob")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("research-backup/roberta-large-semeval2012-average-prompt-d-loob") model = AutoModel.from_pretrained("research-backup/roberta-large-semeval2012-average-prompt-d-loob", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 588 Bytes
0972499 | 1 | {"model": "roberta-large", "max_length": 64, "mode": "average", "data": "relbert/semeval2012_relational_similarity", "template_mode": "manual", "template": "I wasn\u2019t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>", "loss_function": "info_loob", "temperature_nce_constant": 0.05, "temperature_nce_rank": {"min": 0.01, "max": 0.05, "type": "linear"}, "epoch": 22, "batch": 128, "lr": 5e-06, "lr_decay": false, "lr_warmup": 1, "weight_decay": 0, "random_seed": 0, "exclude_relation": null, "n_sample": 640, "gradient_accumulation": 8} |