FERNET-C5-csfever

('---\ndatasets:\n- ctu-aic/csfever\nlanguages:\n- cs\nlicense: cc-by-sa-4.0\ntags:\n- natural-language-inference\n\n---',)

🦾 FERNET-C5-csfever

Transformer model for Natural Language Inference in ['cs'] languages finetuned on ['ctu-aic/csfever'] 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/FERNET-C5-csfever')
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/FERNET-C5-csfever")
tokenizer = AutoTokenizer.from_pretrained("ctu-aic/FERNET-C5-csfever")

🌳 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

cc-by-sa-4.0

💬 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}
}