Instructions to use fav-kky/FERNET-C5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fav-kky/FERNET-C5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fav-kky/FERNET-C5")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fav-kky/FERNET-C5") model = AutoModelForMaskedLM.from_pretrained("fav-kky/FERNET-C5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: "cs" | |
| tags: | |
| - Czech | |
| - KKY | |
| - FAV | |
| license: "cc-by-nc-sa-4.0" | |
| # FERNET-C5 | |
| FERNET-C5 (**F**lexible **E**mbedding **R**epresentation **NET**work) is a monolingual Czech BERT-base model pre-trained from 93GB of Czech Colossal Clean Crawled Corpus (C5). See our paper for details. | |
| We released also a successor of this model based on the RoBERTa architecture [fav-kky/FERNET-C5-RoBERTa](https://huggingface.co/fav-kky/FERNET-C5-RoBERTa). | |
| ## Paper | |
| https://link.springer.com/chapter/10.1007/978-3-030-89579-2_3 | |
| The preprint of our paper is available at https://arxiv.org/abs/2107.10042. | |
| ## Citation | |
| If you find this model useful, please cite our paper: | |
| ``` | |
| @inproceedings{FERNETC5, | |
| title = {Comparison of Czech Transformers on Text Classification Tasks}, | |
| author = {Lehe{\v{c}}ka, Jan and {\v{S}}vec, Jan}, | |
| year = 2021, | |
| booktitle = {Statistical Language and Speech Processing}, | |
| publisher = {Springer International Publishing}, | |
| address = {Cham}, | |
| pages = {27--37}, | |
| doi = {10.1007/978-3-030-89579-2_3}, | |
| isbn = {978-3-030-89579-2}, | |
| editor = {Espinosa-Anke, Luis and Mart{\'i}n-Vide, Carlos and Spasi{\'{c}}, Irena} | |
| } | |
| ``` |