Instructions to use fgaim/tibert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fgaim/tibert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fgaim/tibert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fgaim/tibert-base") model = AutoModelForMaskedLM.from_pretrained("fgaim/tibert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# BERT Base for Tigrinya Language
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We pretrain a BERT base-uncased model on a relatively small dataset for Tigrinya (34M tokens) for 40 epochs.
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Contained in this card is a PyTorch model exported from the original model that was trained on a TPU v3.8 with Flax.
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## Hyperparameters
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The hyperparameters corresponding to model sizes mentioned above are as follows:
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| Model Size | L | AH | HS | FFN | P |
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|------------|----|----|-----|------|------|
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| BASE | 12 | 12 | 768 | 3072 | 110M |
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(L = number of layers; AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
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