GreekDeBERTa-base
GreekDeBERTa-base is a language model specifically pre-trained for Greek Natural Language Processing (NLP) tasks. It is based on the DeBERTa architecture and is pre-trained on Masked Language Modeling (MLM).
Model Details
- Model Architecture: DeBERTa-base
- Language: Greek
- Pre-training Objectives: - Masked Language Modeling (MLM)
- Tokenizer: SentencePiece Model (
spm.model)
Model Files
The following files are included in the repository:
config.json: The model configuration file used by the DeBERTa-base architecture.pytorch_model.bin: The pre-trained model weights in PyTorch format.spm.model: The SentencePiece model file used for tokenization.vocab.txt: A human-readable vocabulary file that contains the list of tokens used by the model.tokenizer_config.json: Configuration file for the tokenizer.
How to Use
You can easily load and use the model in Python with the Hugging Face transformers library. Below is an example to get started with token classification:
from transformers import AutoTokenizer, AutoModelForTokenClassification
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("AI-team-UoA/GreekDeBERTa-base")
model = AutoModelForTokenClassification.from_pretrained("AI-team-UoA/GreekDeBERTa-base")
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