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- ---
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- tags:
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- - generated_from_keras_callback
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- model-index:
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- - name: xlm-roberta-large-squad2-ctkfacts
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- results: []
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- ---
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- <!-- This model card has been generated automatically according to the information Keras had access to. You should
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- probably proofread and complete it, then remove this comment. -->
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- # xlm-roberta-large-squad2-ctkfacts
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-
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- This model was trained from scratch on an unknown dataset.
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- It achieves the following results on the evaluation set:
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-
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-
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- ## Model description
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-
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- More information needed
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-
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- ## Intended uses & limitations
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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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- More information needed
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-
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- ## Training procedure
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-
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- ### Training hyperparameters
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-
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- The following hyperparameters were used during training:
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- - optimizer: None
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- - training_precision: float32
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-
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- ### Training results
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-
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-
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-
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- ### Framework versions
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-
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- - Transformers 4.21.0
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- - TensorFlow 2.7.1
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- - Datasets 2.4.0
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- - Tokenizers 0.12.1
 
 
 
 
 
 
 
 
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+ # 🦾 xlm-roberta-large-squad2-ctkfacts
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+
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+ ## 🧰 Usage
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+
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+ ### 🤗 Using Huggingface `transformers`
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+ ```python
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+ from transformers import <class 'transformers.models.auto.modeling_auto.AutoModelForSequenceClassification'>, <class 'transformers.models.auto.tokenization_auto.AutoTokenizer'>
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+ model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/xlm-roberta-large-squad2-ctkfacts")
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+ tokenizer = AutoTokenizer.from_pretrained("ctu-aic/xlm-roberta-large-squad2-ctkfacts")
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+ ```
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+
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+ ### 👾 Using UKPLab `sentence_transformers` `CrossEncoder`
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+ The model was trained using the `CrossEncoder` API and we recommend it for its usage.
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+ ```python
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+ from sentence_transformers.cross_encoder import CrossEncoder
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+ model = CrossEncoder('ctu-aic/xlm-roberta-large-squad2-ctkfacts')
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+ scores = model.predict([["My first context.", "My first hypothesis."],
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+ ["Second context.", "Hypothesis."]])
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+ ```
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+
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+ ## 🌳 Contributing
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+ Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
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+
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+ ## 👬 Authors
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+ 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))
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+
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+ The code was codeveloped by the NLP team at Artificial Intelligence Center of CTU in Prague ([AIC](https://www.aic.fel.cvut.cz/)).
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+
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+ ## 🔐 License
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+ [cc-by-sa-4.0](https://choosealicense.com/licenses/cc-by-sa-4.0)
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+
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+ ## 💬 Citation
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+ If you find this model helpful, feel free to cite our publication:
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+ ```
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+
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+ @article{DBLP:journals/corr/abs-2201-11115,
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+ author = {Jan Drchal and
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+ Herbert Ullrich and
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+ Martin R{'{y}}par and
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+ Hana Vincourov{'{a}} and
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+ V{'{a}}clav Moravec},
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+ title = {CsFEVER and CTKFacts: Czech Datasets for Fact Verification},
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+ journal = {CoRR},
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+ volume = {abs/2201.11115},
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+ year = {2022},
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+ url = {https://arxiv.org/abs/2201.11115},
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+ eprinttype = {arXiv},
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+ eprint = {2201.11115},
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+ timestamp = {Tue, 01 Feb 2022 14:59:01 +0100},
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+ biburl = {https://dblp.org/rec/journals/corr/abs-2201-11115.bib},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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+
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+ ```
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