readme: ('---\ndatasets:\n- ctu-aic/csfever\nlanguages:\n- cs\nlicense: cc-by-sa-4.0\ntags:\n- natural-language-inference\n\n---',)
  
  # 🦾 xlm-roberta-large-xnli-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.
  ```python
  from sentence_transformers.cross_encoder import CrossEncoder
  model = CrossEncoder('ctu-aic/xlm-roberta-large-xnli-csfever')
  scores = model.predict([["My first context.", "My first hypothesis."],  
                          ["Second context.", "Hypothesis."]])
  ```
  
  ### 🤗 Using Huggingface `transformers`
  ```python
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/xlm-roberta-large-xnli-csfever")
  tokenizer = AutoTokenizer.from_pretrained("ctu-aic/xlm-roberta-large-xnli-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](https://udb.fel.cvut.cz/?uid=ullriher&sn=&givenname=&_cmd=Hledat&_reqn=1&_type=user&setlang=en)** (e-mail: [ullriher@fel.cvut.cz](mailto:ullriher@fel.cvut.cz))
  
  The code was codeveloped by the NLP team at Artificial Intelligence Center of CTU in Prague ([AIC](https://www.aic.fel.cvut.cz/)).
  
  ## 🔐 License
  [cc-by-sa-4.0](https://choosealicense.com/licenses/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}
  }
  
  ```
  
  