Fill-Mask
Transformers
Safetensors
English
roberta
law
legal
australia
Generated from Trainer
feature-extraction
Eval Results (legacy)
Instructions to use isaacus/emubert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use isaacus/emubert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="isaacus/emubert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("isaacus/emubert") model = AutoModelForMaskedLM.from_pretrained("isaacus/emubert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Fixing typos.
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Fixing typos.
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Fixing typos.
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Noted that the quality of EmuBert's embeddings may not be
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Adjusted emissions by current month gCO₂eq/kWh in Victoria rather than current live gCO₂eq/kWh as training occured over multiple days.
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Excluded carbon emissions from unpublished iterations from reporting.
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Added estimated CO2 emissions of training.
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Changed example tasks.
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Added a progress bar to the code example.
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Further expanded documentation of biases.
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Expanded documentation of biases.
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Added another example.
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Expanded benchmarks.
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Removed Albert from benchmarks as it is too small and its perplexity is high.
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Reattempt at updating task to `fill-mask`.
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Updated task.
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Fixed display of logo.
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Init.
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initial commit
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