Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
Bulgarian
Macedonian
multilingual
roberta
BERTovski
MaCoCu
Instructions to use MaCoCu/BERTovski with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaCoCu/BERTovski with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MaCoCu/BERTovski")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MaCoCu/BERTovski") model = AutoModel.from_pretrained("MaCoCu/BERTovski", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# Benchmark performance
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We tested the performance of BERTovski on benchmarks of XPOS, UPOS and NER. For Bulgarian, we used the data from the [Universal Dependencies](https://universaldependencies.org/) project. For Macedonian, we used the data sets created in the [babushka-bench](https://github.com/clarinsi/babushka-bench/) project. We compare performance to the strong multi-lingual models XLMR-base and XLMR-large. For details regarding the fine-tuning procedure you can checkout our [Github](https://github.com/macocu/LanguageModels).
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Scores are averages of three runs. We use the same hyperparameter settings for all models.
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# Benchmark performance
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We tested the performance of BERTovski on benchmarks of XPOS, UPOS and NER. For Bulgarian, we used the data from the [Universal Dependencies](https://universaldependencies.org/) project. For Macedonian, we used the data sets created in the [babushka-bench](https://github.com/clarinsi/babushka-bench/) project. We also tested on a human translated version of the COPA data set. We compare performance to the strong multi-lingual models XLMR-base and XLMR-large. For details regarding the fine-tuning procedure you can checkout our [Github](https://github.com/macocu/LanguageModels).
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Scores are averages of three runs. We use the same hyperparameter settings for all models.
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