('---\ndatasets:\n- ctu-aic/ctkfacts_nli\nlanguages:\n- cs\nlicense: cc-by-sa-4.0\ntags:\n- natural-language-inference\n\n---',)
🦾 xlm-roberta-large-squad2-ctkfacts_nli
Transformer model for Natural Language Inference in ['cs'] languages finetuned on ['ctu-aic/ctkfacts_nli'] datasets.
🧰 Usage
👾 Using UKPLab sentence_transformers CrossEncoder
The model was trained using the CrossEncoder API and we recommend it for its usage.
from sentence_transformers.cross_encoder import CrossEncoder
model = CrossEncoder('ctu-aic/xlm-roberta-large-squad2-ctkfacts_nli')
scores = model.predict([["My first context.", "My first hypothesis."],
["Second context.", "Hypothesis."]])
🤗 Using Huggingface transformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/xlm-roberta-large-squad2-ctkfacts_nli")
tokenizer = AutoTokenizer.from_pretrained("ctu-aic/xlm-roberta-large-squad2-ctkfacts_nli")
🌳 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
👬 Authors
The model was trained and uploaded by ullriher (e-mail: ullriher@fel.cvut.cz)
The code was codeveloped by the NLP team at Artificial Intelligence Center of CTU in Prague (AIC).
🔐 License
💬 Citation
If you find this repository helpful, feel free to cite our publication:
@article{DBLP:journals/corr/abs-2201-11115,
author = {Herbert Ullrich and
Jan Drchal and
Martin R{'{y}}par and
Hana Vincourov{'{a}} and
V{'{a}}clav Moravec},
title = {CsFEVER and CTKFacts: Acquiring Czech Data for Fact Verification},
journal = {CoRR},
volume = {abs/2201.11115},
year = {2022},
url = {https://arxiv.org/abs/2201.11115},
eprinttype = {arXiv},
eprint = {2201.11115},
timestamp = {Tue, 01 Feb 2022 14:59:01 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-11115.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}