Text Classification
Transformers
PyTorch
Safetensors
Arabic
bert
custom_code
text-embeddings-inference
Instructions to use tunis-ai/TunBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tunis-ai/TunBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tunis-ai/TunBERT", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tunis-ai/TunBERT", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("tunis-ai/TunBERT", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
The tokenizer vocab contains mosly English words and latin script rather than Arabic
#6
by issam9 - opened
Hi,
It seems that the tokenizer is not trained on text with mainly Arabic script. When applied to Arabic text it comes out over segmented and the performance of the model on my task is a lot worse compared to other Arabic models. When I checked vocab.txt file it seems to contain mostly English tokens.
@issam9 their script leverages a pretrained checkpoint, see https://github.com/instadeepai/tunbert/blob/main/models/bert-nvidia/configs/sentiment_analysis_config.yaml#L29 for more details.
let me know if this answers your questions