Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics
Paper • 2110.01518 • Published
How to use prajjwal1/roberta-large-mnli with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="prajjwal1/roberta-large-mnli") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("prajjwal1/roberta-large-mnli")
model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/roberta-large-mnli")YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
If you use the model, please consider citing the paper
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Original Implementation and more info can be found in this Github repository.
Roberta-large trained on MNLI.
| Task | Accuracy |
|---|---|
| MNLI | 90.15 |
| MNLI-mm | 90.02 |
You can also check out:
prajjwal1/roberta-base-mnliprajjwal1/roberta-large-mnliprajjwal1/albert-base-v2-mnliprajjwal1/albert-base-v1-mnliprajjwal1/albert-large-v2-mnli