Fill-Mask
Transformers
PyTorch
Safetensors
Urdu
roberta
urdu
masked-language-modeling
encoder
dunbaabert
Instructions to use DunbaaBERT/DunbaaBERT_32k_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunbaaBERT/DunbaaBERT_32k_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="DunbaaBERT/DunbaaBERT_32k_base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("DunbaaBERT/DunbaaBERT_32k_base") model = AutoModelForMaskedLM.from_pretrained("DunbaaBERT/DunbaaBERT_32k_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: mit
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# DunbaaBERT
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## Pre-training
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- 32k vocab size
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- 100k training steps
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- computed on 2x H100 with 8k batch size
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## Fairseq Checkpoint
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Get the fairseq checkpoint [here](https://drive.proton.me/urls/CBZ3JEK138#ACWZfQGdVev0).
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